U.S. patent application number 15/650942 was filed with the patent office on 2018-01-18 for system and method for identifying language register.
The applicant listed for this patent is Ron Zass. Invention is credited to Ron Zass.
Application Number | 20180018987 15/650942 |
Document ID | / |
Family ID | 60940697 |
Filed Date | 2018-01-18 |
United States Patent
Application |
20180018987 |
Kind Code |
A1 |
Zass; Ron |
January 18, 2018 |
SYSTEM AND METHOD FOR IDENTIFYING LANGUAGE REGISTER
Abstract
System and method for analyzing audio data are provided. The
audio data may be analyzed7 to identify language register. For
example, the audio data may be analyzed to identify language
register of a selected speaker, such as the language register of a
wearer of a wearable audio sensor, of a speaker engaged in
conversation with the wearer of the wearable audio sensor, and so
forth. For example, the audio data may be analyzed to obtain
textual information, and the textual information may be analyzed to
identify the language register. Feedbacks and reports may be
provided based on the identified language register.
Inventors: |
Zass; Ron; (Kiryat Tivon,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Zass; Ron |
Kiryat Tivon |
|
IL |
|
|
Family ID: |
60940697 |
Appl. No.: |
15/650942 |
Filed: |
July 16, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62363261 |
Jul 16, 2016 |
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62444709 |
Jan 10, 2017 |
|
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62460783 |
Feb 18, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04R 1/265 20130101;
A61B 2562/0219 20130101; H04R 2225/43 20130101; G10L 25/63
20130101; H04R 2201/023 20130101; A61B 2562/0204 20130101; H04R
3/005 20130101; G06K 9/00228 20130101; G06K 9/00369 20130101; H04R
5/0335 20130101; G10L 17/00 20130101; A61B 5/1128 20130101; G06F
40/00 20200101; G10L 15/1822 20130101; G06K 9/00362 20130101; G16H
50/70 20180101; G06F 40/10 20200101; G10L 17/26 20130101; G06K
9/00275 20130101; G10L 21/0364 20130101; G10L 21/028 20130101; A61B
5/16 20130101; G01N 2800/28 20130101; G10L 21/0224 20130101; A61N
1/36082 20130101; G10L 25/72 20130101; G06K 9/46 20130101; A61B
5/1114 20130101; H04R 25/407 20130101; A61B 5/02055 20130101; H04R
1/406 20130101 |
International
Class: |
G10L 21/028 20130101
G10L021/028; G10L 15/18 20130101 G10L015/18; H04R 3/00 20060101
H04R003/00; H04R 25/00 20060101 H04R025/00 |
Claims
1. A system for processing audio, the system comprising: at least
one processing unit configured to: obtain audio data captured by
one or more audio sensors; and analyze the audio data to obtain
language register information.
2. The system of claim 1, wherein the at least one processing unit
is further configured to: analyze the audio data to obtain textual
information; and analyze the textual information to obtain the
language register information.
3. The system of claim 1, wherein the at least one processing unit
is further configured to: obtain additional audio data captured by
the one or more audio sensors after the analysis of the audio data;
analyze the additional audio data to obtain additional language
register information; and provide one or more reports to a user
based on at least part of the language register information and at
least part of the additional language register information.
4. The system of claim 1, wherein the one or more audio sensors are
included in a wearable apparatus; the system includes the wearable
apparatus; obtaining the audio data comprises capturing the audio
data from an environment of a wearer of the wearable apparatus
using the one or more audio sensors; and wherein the at least one
processing unit is further configured to: provide feedback to the
wearer based on the language register information.
5. The system of claim 1, wherein the at least one processing unit
is further configured to analyze the audio data to identify a first
group of one or more portions of the audio data associated with a
first speaker; and wherein the language register information is
associated with the first group of one or more portions.
6. The system of claim 5, wherein the one or more audio sensors are
included in a wearable apparatus; the system includes the wearable
apparatus; obtaining the audio data comprises capturing the audio
data from an environment of the wearer of the wearable apparatus
using the one or more audio sensors; and wherein the at least one
processing unit is further configured to: provide feedback to the
wearer based on the language register information.
7. The system of claim 5, wherein the at least one processing unit
is further configured to: analyze the audio data to identify a
second group of one or more portions of the audio data associated
with a second speaker; and analyze the audio data to obtain second
language register information associated with the second group of
one or more portions.
8. The system of claim 7, wherein the at least one processing unit
is further configured to: determine that the first speaker and the
second speaker are engaged in conversation.
9. The system of claim 7, wherein the one or more audio sensors are
included in a wearable apparatus; the system includes the wearable
apparatus; obtaining the audio data comprises capturing the audio
data from an environment of the wearer of the wearable apparatus
using the one or more audio sensors; and wherein the at least one
processing unit is further configured to: assess the language
register information according to the second language register
information to obtain language register assessment result; and
provide feedback to the wearer based on the language register
assessment result.
10. The system of claim 7, wherein the at least one processing unit
is further configured to: assess the language register information
according to the second language register information to obtain
language register assessment result; and provide one or more
reports to a user based on the language register assessment
result.
11. A method for processing audio, the method comprising: obtaining
audio data captured by one or more audio sensors; and analyzing the
audio data to obtain language register information.
12. The method of claim 11, further comprising: analyzing the audio
data to obtain textual information; and analyzing the textual
information to obtain the language register information.
13. The method of claim 11, further comprising: obtaining
additional audio data captured by the one or more audio sensors
after the analysis of the audio data; analyzing the additional
audio data to obtain additional language register information; and
providing one or more reports to a user based on at least part of
the language register information and at least part of the
additional language register information.
14. The method of claim 11, wherein the one or more audio sensors
are included in a wearable apparatus; obtaining the audio data
comprises capturing the audio data from an environment of a wearer
of the wearable apparatus using the one or more audio sensors; and
wherein the method further comprising: providing feedback to the
wearer based on the language register information.
15. The method of claim 11, further comprising analyzing the audio
data to identify a first group of one or more portions of the audio
data associated with a first speaker; and wherein the language
register information is associated with the first group of one or
more portions.
16. The method of claim 15, wherein the one or more audio sensors
are included in a wearable apparatus; obtaining the audio data
comprises capturing the audio data from an environment of a wearer
of the wearable apparatus using the one or more audio sensors; and
wherein the method further comprising: providing feedback to the
wearer based on the language register information.
17. The method of claim 15, further comprising: analyzing the audio
data to identify a second group of one or more portions of the
audio data associated with a second speaker; and analyzing the
audio data to obtain second language register information
associated with the second group of one or more portions.
18. The method of claim 17, further comprising: determine that the
first speaker and the second speaker are engaged in
conversation.
19. The method of claim 17, further comprising: assessing the
language register information according to the second language
register information to obtain language register assessment result;
and providing information to a user based on the language register
assessment result.
20. A non-transitory computer readable medium storing data and
computer implementable instructions for carrying out the method of
claim 11.
Description
CROSS REFERENCES TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority of U.S.
Provisional Patent Application No. 62/363,261, filed on Jul. 16,
2016, U.S. Provisional Patent Application No. 62/444,709, filed on
Jan. 10, 2017, and U.S. Provisional Patent Application No.
62/460,783, filed on Feb. 18, 2017, the disclosures of which
incorporated herein by reference in their entirety.
BACKGROUND
Technological Field
[0002] The disclosed embodiments generally relate to systems and
methods for processing audio. More particularly, the disclosed
embodiments relate to systems and methods for processing audio to
identify language register.
Background Information
[0003] Audio as well as other sensors are now part of numerous
devices, from intelligent personal assistant devices to mobile
phones, and the availability of audio data and other information
produced by these devices is increasing.
[0004] Individuals with autism and Asperger syndrome may have
difficulty or inability to adjust their language register in a
socially appropriate manner.
SUMMARY
[0005] In some embodiments, a system and a method for capturing and
processing audio data from the environment of a person are
provided. The audio data may be analyzed. In some examples,
feedbacks may be provided, for example with regard to conversations
detected in the audio data. In some examples, reports may be
produced, for example based on conversations detected in the audio
data. In some embodiments the system may include a wearable
apparatus configured to be worn by a wearer.
[0006] In some embodiments, additional input sensors may be used,
for example to detect and interpret nonverbal communication. For
example, the additional input sensors may include image
sensors.
[0007] In some embodiments, a method and a system for identifying
language register are provided. Audio data captured by audio
sensors may be obtained. The audio data may be analyzed to obtain
language register information.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIGS. 1A, 1B, 1C, 1D, 1E and 1F are schematic illustrations
of some examples of a user wearing a wearable apparatus.
[0009] FIGS. 2A and 2B are block diagrams illustrating some
possible implementation of a communication system.
[0010] FIGS. 3A and 3B are block diagrams illustrating some
possible implementation of an apparatus.
[0011] FIG. 4 is a block diagram illustrating a possible
implementation of a server.
[0012] FIGS. 5A and 5B are block diagrams illustrating some
possible implementation of a cloud platform.
[0013] FIG. 5C is a block diagram illustrating a possible
implementation of a computational node.
[0014] FIGS. 6A and 6B illustrate exemplary embodiments of memory
containing software modules.
[0015] FIG. 7 illustrates an example of a process for analyzing
audio to obtain language register information.
DESCRIPTION
[0016] Unless specifically stated otherwise, as apparent from the
following discussions, it is appreciated that throughout the
specification discussions utilizing terms such as "processing",
"calculating", "computing", "determining", "generating", "setting",
"configuring", "selecting", "defining", "applying", "obtaining",
"monitoring", "providing", "identifying", "segmenting",
"classifying", "analyzing", "associating", "extracting", "storing",
"receiving", "transmitting", or the like, include action and/or
processes of a computer that manipulate and/or transform data into
other data, said data represented as physical quantities, for
example such as electronic quantities, and/or said data
representing the physical objects.
[0017] The terms "computer", "processor", "controller", "processing
unit", "computing unit", "processing device", and " processing
module" should be expansively construed to cover any kind of
electronic device, component or unit with data processing
capabilities, including, by way of non-limiting example, a personal
computer, a wearable computer, a tablet, a smartphone, a server, a
computing system, a cloud computing platform, a communication
device, a processor (for example, digital signal processor (DSP),
an image signal processor (ISR), a microcontroller, a field
programmable gate array (FPGA), an application specific integrated
circuit (ASIC), a central processing unit (CPA), a graphics
processing unit (GPU), a visual processing unit (VPU), and so on),
possibly with embedded memory, a single core processor, a multi
core processor, a core within a processor, any other electronic
computing device, or any combination of the above.
[0018] The operations in accordance with the teachings herein may
be performed by a computer specially constructed or programmed to
perform the described functions.
[0019] As used herein, the phrase "for example," "such as", "for
instance" and variants thereof describe non-limiting embodiments of
the presently disclosed subject matter. Reference in the
specification to "one case", "some cases", "other cases" or
variants thereof means that a particular feature, structure or
characteristic described in connection with the embodiment(s) may
be included in at least one embodiment of the presently disclosed
subject matter. Thus the appearance of the phrase "one case", "some
cases", "other cases" or variants thereof does not necessarily
refer to the same embodiment(s). As used herein, the term "and/or"
includes any and all combinations of one or more of the associated
listed items.
[0020] It is appreciated that certain features of the presently
disclosed subject matter, which are, for clarity, described in the
context of separate embodiments, may also be provided in
combination in a single embodiment. Conversely, various features of
the presently disclosed subject matter, which are, for brevity,
described in the context of a single embodiment, may also be
provided separately or in any suitable sub-combination.
[0021] One or more stages illustrated in the drawings may be
executed in a different order and/or one or more groups of stages
may be executed simultaneously and vice versa. The drawings
illustrate a general schematic of the system architecture in
accordance embodiments of the presently disclosed subject matter.
Each module in the drawings can be made up of any combination of
software, hardware and/or firmware that performs the functions as
defined and explained herein. The modules in the drawings may be
centralized in one location or dispersed over more than one
location.
[0022] It should be noted that some examples of the presently
disclosed subject matter are not limited in application to the
details of construction and the arrangement of the components set
forth in the following description or illustrated in the drawings.
The invention can be capable of other embodiments or of being
practiced or carried out in various ways. Also, it is to be
understood that the phraseology and terminology employed herein is
for the purpose of description and should not be regarded as
limiting. For example, substitutions, additions or modifications
may be made to the components illustrated in the drawings, and the
illustrative methods described herein may be modified by
substituting, reordering, removing, or adding steps to the
disclosed methods. Accordingly, the following detailed description
is not limited to the disclosed embodiments and examples. Instead,
the proper scope is defined by the appended claims.
[0023] Wherever possible, the same reference numbers are used in
the drawings and the following description to refer to the same or
similar parts.
[0024] The drawings in this document may not be to any scale.
Different drawings may use different scales and different scales
can be used even within the same drawing, for example different
scales for different views of the same object or different scales
for the two adjacent objects.
[0025] FIG. 1A is a schematic illustration of an example of user
111 wearing wearable apparatus or a part of a wearable apparatus
121. In this example, wearable apparatus or a part of a wearable
apparatus 121 may be physically connected or integral to a garment,
and user 111 may wear the garment.
[0026] FIG. 1B is a schematic illustration of an example of user
112 wearing wearable apparatus or a part of a wearable apparatus
122. In this example, wearable apparatus or a part of a wearable
apparatus 122 may be physically connected or integral to a belt,
and user 112 may wear the belt.
[0027] FIG. 1C is a schematic illustration of an example of user
113 wearing wearable apparatus or a part of a wearable apparatus
123. In this example, wearable apparatus or a part of a wearable
apparatus 123 may be physically connected or integral to a wrist
strap, and user 113 may wear the wrist strap.
[0028] FIG. 1D is a schematic illustration of an example of user
114 wearing wearable apparatus or a part of a wearable apparatus
124. In this example, wearable apparatus or a part of a wearable
apparatus 124 may be physically connected or integral to a necklace
134, and user 114 may wear necklace 134.
[0029] FIG. 1E is a schematic illustration of an example of user
115 wearing wearable apparatus or a part of a wearable apparatus
121, wearable apparatus or a part of a wearable apparatus 122, and
wearable apparatus or a part of a wearable apparatus 125. In this
example, wearable apparatus or a part of a wearable apparatus 122
may be physically connected or integral to a belt, and user 115 may
wear the belt. In this example, wearable apparatus or a part of a
wearable apparatus 121 and wearable apparatus or a part of a
wearable apparatus 125 may be physically connected or integral to a
garment, and user 115 may wear the garment.
[0030] FIG. 1F is a schematic illustration of an example of user
116 wearing wearable apparatus or a part of a wearable apparatus
126. In this example, wearable apparatus or a part of a wearable
apparatus 126 may be physically connected to an ear of user 116. In
some examples, wearable apparatus or a part of a wearable apparatus
126 may be physically connected to the left ear and/or right ear of
user 116. In some examples, user 116 may wear two wearable
apparatuses 126, where one wearable apparatus 126 may be connected
to the left ear of user 116, and the second wearable apparatus 126
may be connected to the right ear of user 116. In some examples,
user 116 may wear a wearable apparatus 126 that has at least two
separate parts, where one part of wearable apparatus 126 may be
connected to the left ear of user 116, and the second part of
wearable apparatus 126 may be connected to the right ear of user
116.
[0031] In some embodiments, a user may wear one or more wearable
apparatuses, such as one or more instances of wearable apparatuses
121, 122, 123, 124, 125, and/or 126. For example, a user may wear
one or more wearable apparatuses that are physically connected or
integral to a garment of the user, such as wearable apparatus 121
and/or wearable apparatus 125. For example, a user may wear one or
more wearable apparatuses that are physically connected or integral
to a belt of the user, such as wearable apparatus 122. For example,
a user may wear one or more wearable apparatuses that are
physically connected or integral to a wrist strap of the user, such
as wearable apparatus 123. For example, a user may wear one or more
wearable apparatuses that are physically connected or integral to a
necklace that the user is wearing, such as wearable apparatus 124.
For example, a user may wear one or more wearable apparatuses that
are physically connected or integral to the left ear and/or right
ear of the user, such as wearable apparatus 126. In some examples,
the one or more wearable apparatuses may communicate and/or
collaborate with one another. For example, the one or more wearable
apparatuses may communicate by wires and/or wirelessly.
[0032] In some embodiments, a user may wear a wearable apparatus,
and the wearable apparatus may comprise two or more separate parts.
For example, the wearable apparatus may comprise parts 121, 122,
123, 124, 125, and/or 126. For example, the wearable apparatus may
comprise one or more parts that are physically connected or
integral to a garment of the user, such as 121 and/or part 125. For
example, the wearable apparatus may comprise one or more parts that
are physically connected or integral to a belt of the user, such as
part 122. For example, the wearable apparatus may comprise one or
more parts that are physically connected or integral to a wrist
strap that the user is wearing, such as part 123. For example, the
wearable apparatus may comprise one or more parts that are
physically connected or integral to a necklace that the user is
wearing, such as part 124. For example, the wearable apparatus may
comprise one or more parts that are physically connected to the
left ear and/or the right ear of the user, such as part 126. In
some examples, the separate parts of the wearable apparatus may
communicate by wires and/or wirelessly.
[0033] In some embodiments, possible implementations of wearable
apparatuses 121, 122, 123, 124, 125, and/or 126 may include
apparatus 300, for example as described in FIGS. 3A and 3B. In some
embodiments, apparatus 300 may comprise two or more separate parts.
For example, apparatus 300 may comprise parts 121, 122, 123, 124,
125, and/or 126. In some examples, the separate parts may
communicate by wires and/or wirelessly.
[0034] FIG. 2A is a block diagram illustrating a possible
implementation of a communicating system. In this example,
apparatuses 300a and 300b may communicate with server 400a, with
server 400b, with cloud platform 500, with each other, and so
forth. Some possible implementations of apparatuses 300a and 300b
may include apparatus 300, for example as described in FIGS. 3A and
3B. Some possible implementations of servers 400a and/or 400b may
include server 400, for example as described in FIG. 4. Some
possible implementations of cloud platform 500 are described in
FIGS. 5A, 5B and 5C. In this example, apparatus 300a and/or
apparatus 300b may communicate directly with mobile phone 211,
tablet 212, and/or personal computer (PC) 213. Apparatus 300a
and/or apparatus 300b may communicate with local router 220
directly, and/or through at least one of mobile phone 211, tablet
212, and/or personal computer (PC) 213. In this example, local
router 220 may be connected to communication network 230. Some
examples of communication network 230 may include the Internet,
phone networks, cellular networks, satellite communication
networks, private communication networks, virtual private networks
(VPN), and so forth. Apparatus 300a and/or apparatus 300b may
connect to communication network 230 through local router 220
and/or directly. Apparatus 300a and/or apparatus 300b may
communicate with other devices, such as servers 400a, server 400b,
cloud platform 500, remote storage 240 and network attached storage
(NAS) 250, and so forth, through communication network 230 and/or
directly.
[0035] FIG. 2B is a block diagram illustrating a possible
implementation of a communicating system. In this example,
apparatus 300a, apparatus 300b and/or apparatus 300c may
communicate with cloud platform 500 and/or with each other through
communication network 230. Possible implementations of apparatuses
300a, 300b and 300c may include apparatus 300, for example as
described in FIGS. 3A and 3B. Some possible implementations of
cloud platform 500 are described in FIGS. 5A, 5B and 5C. Some
examples of communication network 230 may include the Internet,
phone networks, cellular networks, satellite communication
networks, private communication networks, virtual private networks
(VPN), and so forth.
[0036] FIGS. 2A and 2B illustrate some possible implementations of
a communication system. In some embodiments, other communication
systems that enable communication between apparatus 300 and server
400 may be used. In some embodiments, other communication systems
that enable communication between apparatus 300 and cloud platform
500 may be used. In some embodiments, other communication systems
that enable communication among a plurality of apparatuses 300 may
be used.
[0037] FIG. 3A is a block diagram illustrating a possible
implementation of apparatus 300. In this example, apparatus 300
comprises: one or more power sources 310; one or more memory units
320; one or more processing units 330; and one or more audio
sensors 360. In some implementations additional components may be
included in apparatus 300, while some components listed above may
be excluded. In some embodiments, power sources 310 and/or audio
sensors 360 may be excluded from the implementation of apparatus
300. In some embodiments, apparatus 300 may further comprise one or
more of the followings: one or more communication modules 340; one
or more audio output units 351; one or more visual outputting units
352; one or more tactile outputting units 353; one or more image
sensors 371; one or more physiological sensors 372; one or more
accelerometers 373; one or more positioning sensors 374; one or
more chemical sensors; one or more temperature sensors; one or more
barometers; one or more environmental sensors; one or more pressure
sensors; one or more proximity sensors; one or more electrical
impedance sensors; one or more electrical voltage sensors; one or
more electrical current sensors; one or more clocks; one or more
user input devices; one or more keyboards; one or more mouses; one
or more touch pads; one or more touch screens; one or more
antennas; one or more output devices; one or more audio speakers;
one or more display screens; one or more augmented reality display
systems; one or more LED indicators; and so forth.
[0038] FIG. 3B is a block diagram illustrating a possible
implementation of apparatus 300. In this example, apparatus 300
comprises: one or more power sources 310; one or more memory units
320; one or more processing units 330; one or more communication
modules 340; one or more audio output units 351; one or more visual
outputting units 352; one or more tactile outputting units 353; one
or more audio sensors 360; one or more image sensors 371; one or
more physiological sensors 372; one or more accelerometers 373; and
one or more positioning sensors 374. In some implementations
additional components may be included in apparatus 300, while some
components listed above may be excluded. In some embodiments, one
or more of the followings may be excluded from the implementation
of apparatus 300: power sources 310; communication modules 340;
audio output units 351; visual outputting units 352; tactile
outputting units 353; audio sensors 360; image sensors 371;
physiological sensors 372; accelerometers 373; and positioning
sensors 374. In some embodiments, apparatus 300 may further
comprise one or more of the followings: one or more chemical
sensors; one or more temperature sensors; one or more barometers;
one or more environmental sensors; one or more pressure sensors;
one or more proximity sensors; one or more electrical impedance
sensors; one or more electrical voltage sensors; one or more
electrical current sensors; one or more clocks; one or more user
input devices; one or more keyboards; one or more mouses; one or
more touch pads; one or more touch screens; one or more antennas;
one or more output devices; one or more audio speakers; one or more
display screens; one or more augmented reality display systems; one
or more LED indicators; and so forth.
[0039] In some embodiments, the one or more power sources 310 may
be configured to: power apparatus 300; power server 400; power
cloud platform 500; power computational node 510; and so forth.
Some possible implementation examples the one or more power sources
310 may comprise: one or more electric batteries; one or more
capacitors; one or more connections to external power sources; one
or more power convertors; one or more electric power generators;
any combination of the above; and so forth.
[0040] In some embodiments, the one or more processing units 330
may be configured to execute software programs, for example
software programs stored in the one or more memory units 320,
software programs received through the one or more communication
modules 340, and so forth. Some possible implementation examples of
processing units 330 may comprise: one or more single core
processors; one or more multicore processors; one or more
controllers; one or more application processors; one or more system
on a chip processors; one or more central processing units; one or
more graphical processing units; one or more neural processing
units; any combination of the above; and so forth. In some
examples, the executed software programs may store information in
memory units 320. In some cases, the executed software programs may
retrieve information from memory units 320.
[0041] In some embodiments, the one or more communication modules
340 may be configured to receive and/or transmit information. Some
possible implementation examples of communication modules 340 may
comprise: wired communication devices; wireless communication
devices; optical communication devices; electrical communication
devices; radio communication devices; sonic and/or ultrasonic
communication devices; electromagnetic induction communication
devices; infrared communication devices; transmitters; receivers;
transmitting and receiving devices; modems; network interfaces;
wireless USB communication devices, wireless LAN communication
devices; Wi-Fi communication devices; LAN communication devices;
USB communication devices; firewire communication devices;
bluetooth communication devices; cellular communication devices,
such as GSM, CDMA, GPRS, W-CDMA, EDGE, CDMA2000, etc.; satellite
communication devices; and so forth.
[0042] In some implementations, control signals and/or
synchronization signals may be transmitted and/or received through
communication modules 340. In some implementations, information
received though communication modules 340 may be stored in memory
units 320. In some implementations, information retrieved from
memory units 320 may be transmitted using communication modules
340. In some implementations, input and/or user input may be
transmitted and/or received through communication modules 340. In
some implementations, audio data may be transmitted and/or received
through communication modules 340, such as audio data captured
using audio sensors 360. In some implementations, visual data, such
as images and/or videos, may be transmitted and/or received through
communication modules 340, such as images and/or videos captured
using image sensors 371. In some implementations, physiological
data may be transmitted and/or received through communication
modules 340, such as physiological data captured using
physiological sensors 372. In some implementations, proper
acceleration information may be transmitted and/or received through
communication modules 340, such as proper acceleration information
captured using accelerometers 373. In some implementations,
positioning information may be transmitted and/or received through
communication modules 340, such as positioning information captured
using positioning sensors 374.
[0043] In some implementations, output information may be
transmitted and/or received through communication modules 340. In
some implementations, audio output information may be transmitted
and/or received through communication modules 340. For example,
audio output information to be outputted using audio outputting
units 351 may be received through communication modules 340. In
some implementations, visual output information may be transmitted
and/or received through communication modules 340. For example,
visual output information to be outputted using visual outputting
units 352 may be received through communication modules 340. In
some implementations, tactile output information may be transmitted
and/or received through communication modules 340. For example,
tactile output information to be outputted using tactile outputting
units 353 may be received through communication modules 340.
[0044] In some embodiments, the one or more audio outputting units
351 may be configured to output audio to a user, for example
through a headset, through one or more audio speakers, and so
forth. In some embodiments, the one or more visual outputting units
352 may be configured to output visual information to a user, for
example through a display screen, through an augmented reality
display system, through a printer, through LED indicators, and so
forth. In some embodiments, the one or more tactile outputting
units 353 may be configured to output tactile feedbacks to a user,
for example through vibrations, through motions, by applying
forces, and so forth. In some examples, output may be provided: in
real time; offline; automatically; periodically; upon request; and
so forth. In some examples, apparatus 300 may be a wearable
apparatus and the output may be provided to: a wearer of the
wearable apparatus; a caregiver of the wearer of the wearable
apparatus; and so forth. In some examples, the output may be
provided to: a caregiver; clinicians; insurers; and so forth.
[0045] In some embodiments, the one or more audio sensors 360 may
be configured to capture audio data. Some possible examples of
audio sensors 360 may include: connectors to microphones;
microphones; unidirectional microphones; bidirectional microphones;
cardioid microphones; omnidirectional microphones; onboard
microphones; wired microphones; wireless microphones; any
combination of the above; and so forth. In some cases, audio data
captured using audio sensors 360 may be stored in memory, for
example in memory units 320. In some cases, audio data captured
using audio sensors 360 may be transmitted, for example using
communication device 340 to an external system, such as server 400,
cloud platform 500, computational node 510, apparatus 300, and so
forth. In some cases, audio data captured using audio sensors 360
may be processed, for example using processing units 330. For
example, the audio data captured using audio sensors 360 may be:
compressed; preprocessed using filters, such as low pass filters,
high pass filters, etc.; downsampled; and so forth. In some cases,
audio data captured using audio sensors 360 may be analyzed, for
example using processing units 330. For example, audio data
captured using audio sensors 360 may be analyzed to identify low
level features, speakers, speech, audio triggers, and so forth. In
another example, audio data captured using audio sensors 360 may be
applied to an inference model.
[0046] In some embodiments, the one or more image sensors 371 may
be configured to capture visual data. Some possible examples of
image sensors 371 may include: CCD sensors; CMOS sensors; stills
image sensors; video image sensors; 2D image sensors; 3D image
sensors; and so forth. Some possible examples of visual data may
include: still images; video clips; continuous video; 2D images; 2D
videos; 3D images; 3D videos; microwave images; terahertz images;
ultraviolet images; infrared images; x-ray images; gamma ray
images; visible light images; microwave videos; terahertz videos;
ultraviolet videos; infrared videos; visible light videos; x-ray
videos; gamma ray videos; and so forth. In some cases, visual data
captured using image sensors 371 may be stored in memory, for
example in memory units 320. In some cases, visual data captured
using image sensors 371 may be transmitted, for example using
communication device 340 to an external system, such as server 400,
cloud platform 500, computational node 510, apparatus 300, and so
forth. In some cases, visual data captured using image sensors 371
may be processed, for example using processing units 330. For
example, the visual data captured using image sensors 371 may be:
compressed; preprocessed using filters, such as low pass filter,
high pass filter, etc.; downsampled; and so forth. In some cases,
visual data captured using image sensors 371 may be analyzed, for
example using processing units 330. For example, visual data
captured using image sensors 371 may be analyzed to identify one or
more of: low level visual features; objects; faces; persons;
events; visual triggers; and so forth. In another example, visual
data captured using image sensors 371 may be applied to an
inference model.
[0047] In some embodiments, the one or more physiological sensors
372 may be configured to capture physiological data. Some possible
examples of physiological sensors 372 may include: glucose sensors;
electrocardiogram sensors; electroencephalogram sensors;
electromyography sensors; odor sensors; respiration sensors; blood
pressure sensors; pulse oximeter sensors; heart rate sensors;
perspiration sensors; and so forth. In some cases, physiological
data captured using physiological sensors 372 may be stored in
memory, for example in memory units 320. In some cases,
physiological data captured using physiological sensors 372 may be
transmitted, for example using communication device 340 to an
external system, such as server 400, cloud platform 500,
computational node 510, apparatus 300, and so forth. In some cases,
physiological data captured using physiological sensors 372 may be
processed, for example using processing units 330. For example, the
physiological data captured using physiological sensors 372 may be
compressed, downsampled, and so forth. In some cases, physiological
data captured using physiological sensors 372 may be analyzed, for
example using processing units 330. For example, physiological data
captured using physiological sensors 372 may be analyzed to
identify events, triggers, and so forth. In another example,
physiological data captured using physiological sensors 372 may be
applied to an inference model.
[0048] In some embodiments, the one or more accelerometers 373 may
be configured to capture proper acceleration information, for
example by: measuring proper acceleration of apparatus 300;
detecting changes in proper acceleration of apparatus 300; and so
forth. In some embodiments, the one or more accelerometers 373 may
comprise one or more gyroscopes. In some cases, information
captured using accelerometers 373 may be stored in memory, for
example in memory units 320. In some cases, information captured
using accelerometers 373 may be transmitted, for example using
communication device 340 to an external system, such as server 400,
cloud platform 500, computational node 510, apparatus 300, and so
forth. In some cases, information captured using accelerometers 373
may be processed, for example using processing units 330. For
example, the information captured using accelerometers 373 may be
compressed, downsampled, and so forth. In some cases, information
captured using accelerometers 373 may be analyzed, for example
using processing units 330. For example, the information captured
using accelerometers 373 may be analyzed to identify events,
triggers, and so forth. In another example, the information
captured using accelerometers 373 may be applied to an inference
model.
[0049] In some embodiments, the one or more positioning sensors 374
may be configured to: obtain positioning information associated
with apparatus 300; detect changes in the position of apparatus
300; and so forth. In some embodiments, the positioning sensors 374
may be implemented using different technologies, such as: Global
Positioning System (GPS); GLObal NAvigation Satellite System
(GLONASS); Galileo global navigation system, BeiDou navigation
system; other Global Navigation Satellite Systems (GNSS); Indian
Regional Navigation Satellite System (IRNSS); Local Positioning
Systems (LPS), Real-Time Location Systems (RTLS); Indoor
Positioning System (IPS); Wi-Fi based positioning systems; cellular
triangulation; and so forth. In some embodiments, the one or more
positioning sensors 374 may comprise one or more altimeters, and be
configured to measure altitude and/or to detect changes in
altitude. In some embodiments, information captured using
positioning sensors 374 may be stored in memory, for example in
memory units 320. In some cases, information captured using
positioning sensors 374 may be transmitted, for example using
communication device 340 to an external system, such as server 400,
cloud platform 500, computational node 510, apparatus 300, and so
forth. In some cases, information captured using positioning
sensors 374 may be processed, for example using processing units
330. For example, the information captured using positioning
sensors 374 may be compressed, downsampled, and so forth. In some
cases, information captured using positioning sensors 374 may be
analyzed, for example using processing units 330. For example, the
information captured using positioning sensors 374 may be analyzed
to identify events, triggers, and so forth. In another example, the
information captured using positioning sensors 374 may be applied
to an inference model.
[0050] FIG. 4 is a block diagram illustrating a possible
implementation of a server 400. In this example, server 400
comprises: one or more power sources 310; one or more memory units
320; one or more processing units 330; and one or more
communication modules 340. In some implementations additional
components may be included in server 400, while some components
listed above may be excluded. In some embodiments, power sources
310 and/or communication modules 340 may be excluded from the
implementation of server 400. In some embodiments, server 400 may
further comprise one or more of the followings: one or more audio
output units 351; one or more visual outputting units 352; one or
more tactile outputting units 353; one or more audio sensors 360;
one or more image sensors 371; one or more accelerometers 373; one
or more positioning sensors 374; one or more chemical sensors; one
or more temperature sensors; one or more barometers; one or more
environmental sensors; one or more pressure sensors; one or more
proximity sensors; one or more electrical impedance sensors; one or
more electrical voltage sensors; one or more electrical current
sensors; one or more clocks; one or more user input devices; one or
more keyboards; one or more mouses; one or more touch pads; one or
more touch screens; one or more antennas; one or more output
devices; one or more audio speakers; one or more display screens;
one or more augmented reality display systems; one or more LED
indicators; and so forth.
[0051] FIG. 5A is a block diagram illustrating a possible
implementation of cloud platform 500. In some examples, cloud
platform 500 may comprise a number of computational nodes, in this
example four computational nodes: computational node 510a,
computational node 510b, computational node 510c and computational
node 510d. In some examples, a possible implementation of
computational nodes 510a, 510b, 510c and/or 510d may comprise
server 400 as described in FIG. 4. In some examples, a possible
implementation of computational nodes 510a, 510b, 510c and/or 510d
may comprise computational node 510 as described in FIG. 5C.
[0052] FIG. 5B is a block diagram illustrating a possible
implementation of cloud platform 500. In this example, cloud
platform 500 comprises: one or more computational nodes 510; one or
more power sources 310; one or more shared memory modules 520; one
or more external communication modules 540; one or more internal
communication modules 550; one or more load balancing modules 560;
and one or more node registration modules 570. In some
implementations additional components may be included in cloud
platform 500, while some components listed above may be excluded.
In some embodiments, one or more of the followings may be excluded
from the implementation of cloud platform 500: power sources 310;
shared memory modules 520; external communication modules 540;
internal communication modules 550; load balancing modules 560; and
node registration modules 570. In some embodiments, cloud platform
500 may further comprise one or more of the followings: one or more
audio output units 351; one or more visual outputting units 352;
one or more tactile outputting units 353; one or more audio sensors
360; one or more image sensors 371; one or more accelerometers 373;
one or more positioning sensors 374; one or more chemical sensors;
one or more temperature sensors; one or more barometers; one or
more environmental sensors; one or more pressure sensors; one or
more proximity sensors; one or more electrical impedance sensors;
one or more electrical voltage sensors; one or more electrical
current sensors; one or more clocks; one or more user input
devices; one or more keyboards; one or more mouses; one or more
touch pads; one or more touch screens; one or more antennas; one or
more output devices; one or more audio speakers; one or more
display screens; one or more augmented reality display systems; one
or more LED indicators; and so forth.
[0053] FIG. 5C is a block diagram illustrating a possible
implementation of computational node 510 of a cloud platform, such
as cloud platform 500. In this example computational node 510
comprises: one or more power sources 310; one or more memory units
320; one or more processing units 330; one or more shared memory
access modules 530; one or more external communication modules 540;
and one or more internal communication modules 550. In some
implementations additional components may be included in
computational node 510, while some components listed above may be
excluded. In some embodiments, one or more of the followings may be
excluded from the implementation of computational node 510: power
sources 310; memory units 320; shared memory access modules 530;
external communication modules 540; and internal communication
modules 550. In some embodiments, computational node 510 may
further comprise one or more of the followings: one or more audio
output units 351; one or more visual outputting units 352; one or
more tactile outputting units 353; one or more audio sensors 360;
one or more image sensors 371; one or more accelerometers 373; one
or more positioning sensors 374; one or more chemical sensors; one
or more temperature sensors; one or more barometers; one or more
environmental sensors; one or more pressure sensors; one or more
proximity sensors; one or more electrical impedance sensors; one or
more electrical voltage sensors; one or more electrical current
sensors; one or more clocks; one or more user input devices; one or
more keyboards; one or more mouses; one or more touch pads; one or
more touch screens; one or more antennas; one or more output
devices; one or more audio speakers; one or more display screens;
one or more augmented reality display systems; one or more LED
indicators; and so forth.
[0054] In some embodiments, external communication modules 540 and
internal communication modules 550 may be implemented as a combined
communication module, for example as communication modules 340. In
some embodiments, one possible implementation of cloud platform 500
may comprise server 400. In some embodiments, one possible
implementation of computational node 510 may comprise server 400.
In some embodiments, one possible implementation of shared memory
access modules 530 may comprise the usage of internal communication
modules 550 to send information to shared memory modules 520 and/or
receive information from shared memory modules 520. In some
embodiments, node registration modules 570 and load balancing
modules 560 may be implemented as a combined module.
[0055] In some embodiments, the one or more shared memory modules
520 may be accessed by more than one computational node. Therefore,
shared memory modules 520 may allow information sharing among two
or more computational nodes 510. In some embodiments, the one or
more shared memory access modules 530 may be configured to enable
access of computational nodes 510 and/or the one or more processing
units 330 of computational nodes 510 to shared memory modules 520.
In some examples, computational nodes 510 and/or the one or more
processing units 330 of computational nodes 510, may access shared
memory modules 520, for example using shared memory access modules
530, in order to perform one or more of: executing software
programs stored on shared memory modules 520; store information in
shared memory modules 520; retrieve information from the shared
memory modules 520; and so forth.
[0056] In some embodiments, the one or more internal communication
modules 550 may be configured to receive information from one or
more components of cloud platform 500, and/or to transmit
information to one or more components of cloud platform 500. For
example, control signals and/or synchronization signals may be sent
and/or received through internal communication modules 550. In
another example, input information for computer programs, output
information of computer programs, and/or intermediate information
of computer programs, may be sent and/or received through internal
communication modules 550. In another example, information received
though internal communication modules 550 may be stored in memory
units 320, in shared memory units 520, and so forth. In an
additional example, information retrieved from memory units 320
and/or shared memory units 520 may be transmitted using internal
communication modules 550. In another example, user input data may
be transmitted and/or received using internal communication modules
550.
[0057] In some embodiments, the one or more external communication
modules 540 may be configured to receive and/or to transmit
information. For example, control signals and/or synchronization
signals may be sent and/or received through external communication
modules 540. In another example, information received though
external communication modules 540 may be stored in memory units
320, in shared memory units 520, and so forth. In an additional
example, information retrieved from memory units 320 and/or shared
memory units 520 may be transmitted using external communication
modules 540. In another example, input data may be transmitted
and/or received using external communication modules 540. Examples
of such input data may include: input data inputted by a user using
user input devices; information captured from the environment of
apparatus 300 using one or more sensors; and so forth. Examples of
such sensors may include: audio sensors 360; image sensors 371;
physiological sensors 372; accelerometers 373; and positioning
sensors 374; chemical sensors; temperature sensors; barometers;
environmental sensors; pressure sensors; proximity sensors;
electrical impedance sensors; electrical voltage sensors;
electrical current sensors; and so forth.
[0058] In some embodiments, the one or more node registration
modules 570 may be configured to track the availability of the
computational nodes 510. In some examples, node registration
modules 570 may be implemented as: a software program, such as a
software program executed by one or more of the computational nodes
510; a hardware solution; a combined software and hardware
solution; and so forth. In some implementations, node registration
modules 570 may communicate with computational nodes 510, for
example using internal communication modules 550. In some examples,
computational nodes 510 may notify node registration modules 570 of
their status, for example by sending messages: at computational
node 510 startups; at computational node 510 shutdowns; at periodic
times; at selected times; in response to queries received from node
registration modules 570; and so forth. In some examples, node
registration modules 570 may query about computational nodes 510
status, for example by sending messages: at node registration
module 570 startup; at periodic times; at selected times; and so
forth.
[0059] In some embodiments, the one or more load balancing modules
560 may be configured to divide the work load among computational
nodes 510. In some examples, load balancing modules 560 may be
implemented as: a software program, such as a software program
executed by one or more of the computational nodes 510; a hardware
solution; a combined software and hardware solution; and so forth.
In some implementations, load balancing modules 560 may interact
with node registration modules 570 in order to obtain information
regarding the availability of the computational nodes 510. In some
implementations, load balancing modules 560 may communicate with
computational nodes 510, for example using internal communication
modules 550. In some examples, computational nodes 510 may notify
load balancing modules 560 of their status, for example by sending
messages: at computational node 510 startups; at computational node
510 shutdowns; at periodic times; at selected times; in response to
queries received from load balancing modules 560; and so forth. In
some examples, load balancing modules 560 may query about
computational nodes 510 status, for example by sending messages: at
load balancing module 560 startup; at periodic times; at selected
times; and so forth.
[0060] FIG. 6A illustrates an exemplary embodiment of memory 600a
containing software modules, and FIG. 6B illustrates an exemplary
embodiment of memory 600b containing software modules. In some
examples, memory 600a may be separate and/or integrated with memory
600b. In addition, memory 600a and memory 600b may be separate from
and/or integrated with memory units 320, separate from and/or
integrated with shared memory modules 520, and so forth. In some
examples, memory 600a and/or memory 600b may be included in a
single device, such as apparatus 300, in server 400, in cloud
platform 500, in computational node 510, and so forth. In some
examples, at least one of memory 600a and memory 600b may be
distributed across several devices, such as one or more apparatuses
300, one or more servers 400, one or more cloud platforms 500, one
or more computational nodes 510, and so forth. Memory 600a and
memory 600b may store more or fewer modules than those shown in
FIG. 6A and 6B. In this example, memory 600a may comprise: module
for obtaining input data (610), module for obtaining audio data
(612), module for obtaining visual data (614), module for obtaining
physiological data (616), module for obtaining positioning data
(618), and module for obtaining motion data (620). In this example,
memory 600b may comprise: module for obtaining textual information
(650), module for obtaining spatial information (652), module for
identifying audio portions (654), module for obtaining prosodic
information (656), module for identifying conversations (658),
module for identifying speakers (660), module for measuring lengths
(664), module for identifying context (680), module for providing
feedbacks (690), and module for providing reports (692). The above
modules may be implemented in software, hardware, firmware, a mix
of any of those, or the like. For example, if the modules are
implemented in software, they may contain software instructions for
execution by at least one processing device, such as processing
unit 330, by apparatus 300, by server 400, by cloud platform 500,
by computational node 510, and so forth.
[0061] In some embodiments, obtaining input data (610) may comprise
one or more of: obtaining audio data and/or preprocessed audio
data, for example using module 612 for obtaining audio data;
obtaining visual data and/or preprocessed visual data, for example
using module 614 for obtaining visual data; obtaining physiological
data and/or preprocessed physiological data, for example using
module 616 for obtaining physiological data; obtaining positioning
data and/or preprocessed positioning data, for example using module
618 for obtaining positioning data; obtaining motion data and/or
preprocessed motion data, for example using module 620 for
obtaining motion data; and so forth. In some embodiments, a user
may wear a wearable apparatus comprising one or more sensors, such
as a wearable version of apparatus 300, and obtaining input data
(610) may comprise obtaining input data captured from the
environment of the user using the input sensors.
[0062] In some embodiments, obtaining audio data (612) may comprise
obtaining and/or capturing audio data from one or more audio
sensors, for example using audio sensors 360. In some examples, the
one or more audio sensors may comprise one or more wearable audio
sensors, such as a wearable version of audio sensors 360. In some
embodiments, obtaining audio data (612) may comprise receiving
audio data from an external device, for example through a
communication device such as communication modules 340, external
communication modules 540, internal communication modules 550, and
so forth. In some embodiments, obtaining audio data (612) may
comprise reading audio data from memory, such as memory units 320,
shared memory modules 520, and so forth. In some embodiments,
obtaining audio data (612) may comprise obtaining audio data
captured: continuously; at selected times; when specific conditions
are met; upon a detection of a trigger; and so forth.
[0063] In some embodiments, obtaining audio data (612) may further
comprise analyzing the audio data to obtain preprocessed audio
data. One of ordinary skill in the art will recognize that the
followings are examples, and that the audio data may be
preprocessed using other kinds of preprocessing methods. In some
examples, the audio data may be preprocessed by transforming the
audio data using a transformation function to obtain a transformed
audio data, and the preprocessed audio data may comprise the
transformed audio data. For example, the transformation function
may comprise a multiplication of a vectored time series
representation of the audio data with a transformation matrix. For
example, the transformation function may comprise convolutions,
audio filters (such as low-pass filters, high-pass filters,
band-pass filters, all-pass filters, etc.), nonlinear functions,
and so forth. In some examples, the audio data may be preprocessed
by smoothing the audio data, for example using Gaussian
convolution, using a median filter, and so forth. In some examples,
the audio data may be preprocessed to obtain a different
representation of the audio data. For example, the preprocessed
audio data may comprise: a representation of at least part of the
audio data in a frequency domain; a Discrete Fourier Transform of
at least part of the audio data; a Discrete Wavelet Transform of at
least part of the audio data; a time/frequency representation of at
least part of the audio data; a spectrogram of at least part of the
audio data; a log spectrogram of at least part of the audio data; a
Mel-Frequency Cepstrum of at least part of the audio data; a
sonogram of at least part of the audio data; a periodogram of at
least part of the audio data; a representation of at least part of
the audio data in a lower dimension; a lossy representation of at
least part of the audio data; a lossless representation of at least
part of the audio data; a time order series of any of the above;
any combination of the above; and so forth. In some examples, the
audio data may be preprocessed to extract audio features from the
audio data. Some examples of such audio features may include:
auto-correlation; number of zero crossings of the audio signal;
number of zero crossings of the audio signal centroid; MP3 based
features; rhythm patterns; rhythm histograms; spectral features,
such as spectral centroid, spectral spread, spectral skewness,
spectral kurtosis, spectral slope, spectral decrease, spectral
roll-off, spectral variation, etc.; harmonic features, such as
fundamental frequency, noisiness, inharmonicity, harmonic spectral
deviation, harmonic spectral variation, tristimulus, etc.;
statistical spectrum descriptors; wavelet features; higher level
features; perceptual features, such as total loudness, specific
loudness, relative specific loudness, sharpness, spread, etc.;
energy features, such as total energy, harmonic part energy, noise
part energy, etc.; temporal features; and so forth.
[0064] In some embodiments, analysis of the audio data may be
performed on the raw audio data and/or on the preprocessed audio
data. In some examples, the analysis of the audio data and/or the
preprocessed audio data may be based, at least in part, on one or
more rules, functions, procedures, neural networks, inference
models, and so forth. The rules, functions, procedures, neural
networks, and inference models may be applied to the raw audio data
and/or to the preprocessed audio data. Some examples of such
inference models may comprise: a classification model; a regression
model; an inference model preprogrammed manually; a result of
training algorithms, such as machine learning algorithms and/or
deep learning algorithms, on training examples, where the training
examples may include examples of data instances, and in some cases,
each data instance may be labeled with a corresponding desired
label and/or result; and so forth.
[0065] In some embodiments, obtaining visual data (614) may
comprise obtaining and/or capturing visual data, such as: images;
video frames; sequence of images; video clips; continuous videos;
3D images; 3D video frames; sequence of 3D images; 3D video clips;
continuous 3D video clips; any combination of the above; and so
forth. In some embodiments, visual data obtained by module 614 may
be synchronized with audio data obtained by module 612. In some
embodiments, obtaining visual data (614) may comprise obtaining
and/or capturing visual data from one or more image sensors, for
example using image sensors 371. In some embodiments, the one or
more image sensors may comprise one or more wearable image sensors,
such as image sensors 371 included a wearable version of apparatus
300. In some embodiments, obtaining visual data (614) may comprise
receiving visual data from an external device, for example through
a communication device such as communication modules 340, external
communication modules 540, internal communication modules 550, and
so forth. In some embodiments, obtaining visual data (614) may
comprise reading visual data from memory, such as memory units 320,
shared memory modules 520, and so forth. In some embodiments,
obtaining visual data (614) may comprise obtaining visual data
captured: continuously; at selected times; when specific conditions
are met; upon a detection of a trigger; and so forth.
[0066] In some embodiments, obtaining visual data (614) may further
comprise analyzing the visual data to obtain preprocessed visual
data. One of ordinary skill in the art will recognize that the
followings are examples, and that the visual data may be
preprocessed using other kinds of preprocessing methods. In some
examples, the visual data may be preprocessed by transforming the
visual data using a transformation function to obtain a transformed
visual data, and the preprocessed visual data may comprise the
transformed visual data. For example, the transformation function
may comprise convolutions, visual filters (such as low-pass
filters, high-pass filters, band-pass filters, all-pass filters,
etc.), nonlinear functions, and so forth. In some examples, the
visual data may be preprocessed by smoothing the visual data, for
example using Gaussian convolution, using a median filter, and so
forth. In some examples, the visual data may be preprocessed to
obtain a different representation of the visual data. For example,
the preprocessed visual data may comprise: a representation of at
least part of the visual data in a frequency domain; a Discrete
Fourier Transform of at least part of the visual data; a Discrete
Wavelet Transform of at least part of the visual data; a
time/frequency representation of at least part of the visual data;
a representation of at least part of the visual data in a lower
dimension; a lossy representation of at least part of the visual
data; a lossless representation of at least part of the visual
data; a time order series of any of the above; any combination of
the above; and so forth. In some examples, the visual data may be
preprocessed to extract edges, and the preprocessed visual data may
comprise information based on and/or related to the extracted
edges. In some examples, the visual data may be preprocessed to
extract visual features from the visual data. Some examples of such
visual features may comprise information based on and/or related
to: edges; corners; blobs; ridges; Scale Invariant Feature
Transform (SIFT) features; temporal features; and so forth.
[0067] In some embodiments, analysis of the visual data may be
performed on the raw visual data and/or on the preprocessed visual
data. In some examples, the analysis of the visual data and/or the
preprocessed visual data may be based, at least in part, on one or
more rules, functions, procedures, neural networks, inference
models, and so forth. The rules, functions, procedures, neural
networks, and inference models may be applied to the raw visual
data and/or to the preprocessed visual data. Some examples of such
inference models may comprise: a classification model; a regression
model; an inference model preprogrammed manually; a result of
training algorithms, such as machine learning algorithms and/or
deep learning algorithms, on training examples, where the training
examples may include examples of data instances, and in some cases,
each data instance may be labeled with a corresponding desired
label and/or result; and so forth.
[0068] In some embodiments, obtaining physiological data (616) may
comprise obtaining and/or capturing physiological data from one or
more physiological sensors, for example using physiological sensors
372. In some examples, one or more physiological sensors may
comprise one or more wearable physiological sensors, such as
physiological sensors 372 included in a wearable version of
apparatus 300. Some examples of such physiological sensors may
include: glucose sensors, electrocardiogram sensors,
electroencephalogram sensors, electromyography sensors, odor
sensors, respiration sensors, blood pressure sensors, pulse
oximeter sensors, heart rate sensors, perspiration sensors, and so
forth. In some embodiments, physiological data obtained by module
616 may be synchronized with audio data obtained by module 612
and/or with visual data obtained by module 614. In some
embodiments, obtaining physiological data (616) may comprise
receiving physiological data from an external device, for example
through a communication device such as communication modules 340,
external communication modules 540, internal communication modules
550, and so forth. In some embodiments, obtaining physiological
data (616) may comprise reading physiological data from memory,
such as memory units 320, shared memory modules 520, and so forth.
In some embodiments, obtaining physiological data (616) may
comprise obtaining physiological data captured: continuously; at
selected times; when specific conditions are met; upon a detection
of a trigger; and so forth.
[0069] In some embodiments, obtaining physiological data (616) may
further comprise analyzing physiological data to obtain
preprocessed physiological data. One of ordinary skill in the art
will recognize that the followings are examples, and that the
physiological data may be preprocessed using other kinds of
preprocessing methods. In some examples, the physiological data may
be preprocessed by transforming the physiological data using a
transformation function to obtain a transformed physiological data,
and the preprocessed physiological data may comprise the
transformed physiological data. For example, the transformation
function may comprise convolutions, filters (such as low-pass
filters, high-pass filters, band-pass filters, all-pass filters,
etc.), nonlinear functions, and so forth. In some examples, the
physiological data may be preprocessed by smoothing the
physiological data, for example using Gaussian convolution, using a
median filter, and so forth. In some examples, the physiological
data may be preprocessed to obtain a different representation of
the physiological data. For example, the preprocessed physiological
data may comprise: a representation of at least part of the
physiological data in a frequency domain; a Discrete Fourier
Transform of at least part of the physiological data; a Discrete
Wavelet Transform of at least part of the physiological data; a
time/frequency representation of at least part of the physiological
data; a representation of at least part of the physiological data
in a lower dimension; a lossy representation of at least part of
the physiological data; a lossless representation of at least part
of the physiological data; a time order series of any of the above;
any combination of the above; and so forth. In some examples, the
physiological data may be preprocessed to detect features within
the physiological data, and the preprocessed physiological data may
comprise information based on and/or related to the detected
features.
[0070] In some embodiments, analysis of the physiological data may
be performed on the raw physiological data and/or on the
preprocessed physiological data. In some examples, the analysis of
the physiological data and/or the preprocessed physiological data
may be based, at least in part, on one or more rules, functions,
procedures, neural networks, inference models, and so forth. The
rules, functions, procedures, neural networks, and inference models
may be applied to the raw physiological data and/or to the
preprocessed physiological data. Some examples of such inference
models may comprise: a classification model; a regression model; an
inference model preprogrammed manually; a result of training
algorithms, such as machine learning algorithms and/or deep
learning algorithms, on training examples, where the training
examples may include examples of data instances, and in some cases,
each data instance may be labeled with a corresponding desired
label and/or result; and so forth.
[0071] In some embodiments, obtaining positioning data (618) may
comprise obtaining and/or capturing positioning data from one or
more sensors, for example using positioning sensors 374. In some
examples, the one or more sensors may comprise one or more wearable
sensors, such as positioning sensors 374 included in a wearable
version of apparatus 300. In some embodiments, positioning data
obtained by module 618 may be synchronized with audio data obtained
by module 612 and/or with visual data obtained by module 614 and/or
with physiological data obtained by module 616. In some
embodiments, obtaining positioning data (618) may comprise
receiving positioning data from an external device, for example
through a communication device such as communication modules 340,
external communication modules 540, internal communication modules
550, and so forth. In some embodiments, obtaining positioning data
(618) may comprise reading positioning data from memory, such as
memory units 320, shared memory modules 520, and so forth. In some
embodiments, obtaining positioning data (618) may comprise
obtaining positioning data captured: continuously; at selected
times; when specific conditions are met; upon a detection of a
trigger; and so forth.
[0072] In some embodiments, obtaining positioning data (618) may
further comprise analyzing positioning data to obtain preprocessed
positioning data. One of ordinary skill in the art will recognize
that the followings are examples, and that the positioning data may
be preprocessed using other kinds of preprocessing methods. In some
examples, the positioning data may be preprocessed by transforming
the positioning data using a transformation function to obtain a
transformed positioning data, and the preprocessed positioning data
may comprise the transformed positioning data. For example, the
transformation function may comprise convolutions, filters (such as
low-pass filters, high-pass filters, band-pass filters, all-pass
filters, etc.), nonlinear functions, and so forth. In some
examples, the positioning data may be preprocessed by smoothing the
positioning data, for example using Gaussian convolution, using a
median filter, and so forth. In some examples, the positioning data
may be preprocessed to obtain a different representation of the
positioning data. For example, the preprocessed positioning data
may comprise: a representation of at least part of the positioning
data in a frequency domain; a Discrete Fourier Transform of at
least part of the positioning data; a Discrete Wavelet Transform of
at least part of the positioning data; a time/frequency
representation of at least part of the positioning data; a
representation of at least part of the positioning data in a lower
dimension; a lossy representation of at least part of the
positioning data; a lossless representation of at least part of the
positioning data; a time order series of any of the above; any
combination of the above; and so forth. In some examples, the
positioning data may be preprocessed to detect features and/or
patterns within the positioning data, and the preprocessed
positioning data may comprise information based on and/or related
to the detected features and/or the detected patterns. In some
examples, the positioning data may be preprocessed by comparing the
positioning data to positions of known sites to determine sites
from the positioning data.
[0073] In some embodiments, analysis of the positioning data may be
performed on the raw positioning data and/or on the preprocessed
positioning data. In some examples, the analysis of the positioning
data and/or the preprocessed positioning data may be based, at
least in part, on one or more rules, functions, procedures, neural
networks, inference models, and so forth. The rules, functions,
procedures, neural networks, and inference models may be applied to
the raw positioning data and/or to the preprocessed positioning
data. Some examples of such inference models may comprise: a
classification model; a regression model; an inference model
preprogrammed manually; a result of training algorithms, such as
machine learning algorithms and/or deep learning algorithms, on
training examples, where the training examples may include examples
of data instances, and in some cases, each data instance may be
labeled with a corresponding desired label and/or result; and so
forth.
[0074] In some embodiments, obtaining motion data (620) may
comprise obtaining and/or capturing motion data from one or more
sensors, for example using accelerometers 373 and/or gyroscopes
and/or positioning sensors 374. In some examples, the one or more
sensors may comprise one or more wearable sensors, such as
accelerometers 373 and/or gyroscopes and/or positioning sensors 374
included in a wearable version of apparatus 300. In some
embodiments, motion data obtained by module 620 may be synchronized
with audio data obtained by module 612 and/or with visual data
obtained by module 614 and/or with physiological data obtained by
module 616 and/or with positioning data obtained by module 618. In
some embodiments, obtaining motion data (620) may comprise
receiving motion data from an external device, for example through
a communication device such as communication modules 340, external
communication modules 540, internal communication modules 550, and
so forth. In some embodiments, obtaining motion data (620) may
comprise reading motion data from memory, such as memory units 320,
shared memory modules 520, and so forth. In some embodiments,
obtaining motion data (620) may comprise obtaining motion data
captured: continuously; at selected times; when specific conditions
are met; upon a detection of a trigger; and so forth.
[0075] In some embodiments, obtaining motion data (620) may further
comprise analyzing motion data to obtain preprocessed motion data.
One of ordinary skill in the art will recognize that the followings
are examples, and that the motion data may be preprocessed using
other kinds of preprocessing methods. In some examples, the motion
data may be preprocessed by transforming the motion data using a
transformation function to obtain a transformed motion data, and
the preprocessed motion data may comprise the transformed motion
data. For example, the transformation function may comprise
convolutions, filters (such as low-pass filters, high-pass filters,
band-pass filters, all-pass filters, etc.), nonlinear functions,
and so forth. In some examples, the motion data may be preprocessed
by smoothing the motion data, for example using Gaussian
convolution, using a median filter, and so forth. In some examples,
the motion data may be preprocessed to obtain a different
representation of the motion data. For example, the preprocessed
motion data may comprise: a representation of at least part of the
motion data in a frequency domain; a Discrete Fourier Transform of
at least part of the motion data; a Discrete Wavelet Transform of
at least part of the motion data; a time/frequency representation
of at least part of the motion data; a representation of at least
part of the motion data in a lower dimension; a lossy
representation of at least part of the motion data; a lossless
representation of at least part of the motion data; a time order
series of any of the above; any combination of the above; and so
forth. In some examples, the motion data may be preprocessed to
detect features and/or motion patterns within the motion data, and
the preprocessed motion data may comprise information based on
and/or related to the detected features and/or the detected motion
patterns.
[0076] In some embodiments, analysis of the motion data may be
performed on the raw motion data and/or on the preprocessed motion
data. In some examples, the analysis of the motion data and/or the
preprocessed motion data may be based, at least in part, on one or
more rules, functions, procedures, neural networks, inference
models, and so forth. The rules, functions, procedures, neural
networks, and inference models may be applied to the raw motion
data and/or to the preprocessed motion data. Some examples of such
inference models may comprise: a classification model; a regression
model; an inference model preprogrammed manually; a result of
training algorithms, such as machine learning algorithms and/or
deep learning algorithms, on training examples, where the training
examples may include examples of data instances, and in some cases,
each data instance may be labeled with a corresponding desired
label and/or result; and so forth.
[0077] In some embodiments, obtaining textual information (650) may
comprise analyzing the audio data and/or the preprocessed audio
data to obtain information, including textual information. In some
examples, obtaining textual information (650) may comprise using
speech to text algorithms to transcribe spoken language in the
audio data. In some examples, obtaining textual information (650)
may comprise: analyzing the audio data and/or the preprocessed
audio data to identify words, keywords, and/or phrases in the audio
data, for example using sound recognition algorithms; and
representing the identified words, keywords, and/or phrases, for
example in a textual manner, using graphical symbols, in a vector
representation, as a pointer to a database of words, keywords,
and/or phrases, and so forth. In some examples, obtaining textual
information (650) may comprise: analyzing the audio data and/or the
preprocessed audio data using sound recognition algorithms to
identify nonverbal sounds in the audio data; and describing the
identified nonverbal sounds, for example in a textual manner, using
graphical symbols, as a pointer to a database of sounds, and so
forth. In some examples, obtaining textual information (650) may
comprise using acoustic fingerprint based algorithms to identify
items in the audio data. Some examples of such items may include:
songs, melodies, tunes, sound effects, and so forth. The identified
items may be represented: in a textual manner; using graphical
symbols; as a pointer to a database of items; and so forth. In some
examples, obtaining textual information (650) may comprise
analyzing the audio data and/or the preprocessed audio data to
obtain properties of voices present in the audio data, including
properties associated with: pitch, intensity, tempo, rhythm,
prosody, flatness, and so forth. In some examples, obtaining
textual information (650) may comprise: recognizing different
voices, for example in different portions of the audio data; and/or
identifying different properties of voices present in different
parts of the audio data. As a result, different portions of the
textual information may be associated with different voices and/or
different properties. In some examples, different portions of the
textual information may be associated with different textual
formats, such as layouts, fonts, font sizes, font styles, font
formats, font typefaces, and so forth. For example, different
portions of the textual information may be associated with
different textual formats based on different voices and/or
different properties associated with the different portions of the
textual information. Some examples of such speech to text
algorithms and/or sound recognition algorithms may include: hidden
Markov models based algorithms; dynamic time warping based
algorithms; neural networks based algorithms; machine learning
and/or deep learning based algorithms; and so forth.
[0078] In some embodiments, obtaining spatial information (652) may
comprise obtaining spatial information associated with the audio
data. In some examples, the obtained spatial information may be
synchronized with the audio data. In some examples, the obtained
spatial information may comprise location information related to
the location of: one or more sound sources associated with sounds
present in the audio data; one or more speakers associated with
speech present in the audio data; and so forth. Some examples of
location information may include information associated with one or
more of: direction; distance; 2D position; 3D position; absolute
position; relative position; any combination of the above; and so
forth. In some examples, location information may be: associated
with a single point in time; associated with multiple points in
time; associated with a range of times; continuous; and so
forth.
[0079] In some embodiments, obtaining spatial information (652) may
comprise analyzing the audio data and/or the preprocessed audio
data to obtain spatial information. In some embodiments, obtaining
spatial information (652) may comprise analyzing the audio data
and/or the preprocessed audio data using sound localization
algorithms to obtain location information associated with sounds
and/or speech present in the audio data. Some examples of sound
localization algorithms may include: steered beamformer approach
based algorithms; collocated microphone array based algorithms;
binaural hearing learning based algorithms; head related transfer
function based algorithms; cross power spectrum phase based
algorithms; 2D sensor line array based algorithms; hierarchical
algorithms; neural networks based algorithms; triangulation
algorithms; time of arrival based algorithms; particle velocity
based algorithms; and so forth. In some embodiments, obtaining
spatial information (652) may comprise obtaining estimated
direction of arrival associated with the audio data, and in some
cases, the location information may be based on the estimated
direction of arrival.
[0080] In some embodiments, obtaining spatial information (652) may
comprise analyzing the visual data and/or the preprocessed visual
data to obtain spatial information, such as: location information
associated with one or more sound sources visible in the visual
data; location information associated with one or more speakers
visible in the visual data; and so forth. In some examples, a
speaker location in 2D image and/or 2D video may be detected using
detection algorithms, for example by face detection algorithms, by
algorithms that detect lips movements, etc., and location
information may be calculated, for example: a direction may be
calculated based on the based on the speaker location in the 2D
image and/or 2D video and/or the capturing parameters; a distance
may be calculated based on the based on the speaker location in the
2D image and/or 2D video and/or the capturing parameters; and so
on. In some examples, a speaker location in 3D image and/or 3D
video may be detected using detection algorithms, therefore
obtaining location information, such as direction, distance,
position, and so forth. In some examples, stereopsis methods may be
applied on the visual data and/or the preprocessed visual data to
obtain the location information.
[0081] In some embodiments, obtaining spatial information (652) may
comprise associating a speaker visible in the visual data with one
or more portions of speech in the audio data. For example,
detection of lips movement at a certain time may hint an
association of the speaker moving the lips with speech present in
the audio data at the same time. In an additional example,
correspondence between an estimated direction associated with the
audio data and an estimated direction of a person and/or a face
appearing in the visual data may hint an association of the person
and/or face with speech present in the audio data at the same time.
In some examples, these hints may be aggregated, and after a
certain confidence threshold is exceeded, a speaker may be
associated with specific portions of speech in the audio data. In
some examples, the confidence level may be based, at least in part,
on correspondence between speaker diarization of the audio data and
on appearance of specific people in the visual data over time, for
example based on tracking algorithms, based on face recognition
algorithms, and so forth. In some examples, a database of
associations of face information with voice profiles may be
accessed, a speaker may be associated with one or more portions of
speech in the audio data that match the speaker voice profile, the
speaker may be detected in the visual data based on the face
information, and an association may be made between the one or more
portions of speech matching the voice profile and information based
on the detection in the visual data.
[0082] In some embodiments, obtaining spatial information (652) may
comprise obtaining directional information associated of one
speaker with respect to another speaker. For example, the
directional information may comprise information associated with at
least one of: relative direction, relative distance, relative
position, and so forth. In some examples, location information for
two speakers may be obtained, for example as described above, and
relative location information of one speaker with respect to
another speaker may be calculated. For example, given direction and
distance of the two speakers from the same point, the relative
direction and distance may be obtain through subtraction of the two
vectors. In another example, given two absolute positions, the
relative position may be obtained through subtraction of one
position from the other. In some cases, the location of a speaker
may be calculated with respect to sensors, such as audio sensors
360 and/or image sensors 371, and in case the sensors are wearable
sensors configured to be worn by one of the speakers, the relative
location of a speaker may be based on the location information
calculated for that speaker.
[0083] In some embodiments, obtaining spatial information (652) may
comprise obtaining spatial orientation information associated with
one or more speakers. For example, spatial orientation information
may be associated with a wearer of a wearable sensor, of a speaker
speaking in the captured audio data, of a person and/or a speaker
visible in the captured visual data, and so forth.
[0084] In some embodiments, information captured using one or more
wearable sensors configured to be worn by a wearer may be obtained,
and the spatial orientation information associated with the wearer
may comprise the orientation of at least one wearable sensor with
respect to the wearer. In some examples, the orientation of the at
least one wearable sensor with respect to the wearer may be
obtained using: an accelerometer, such as accelerometer 373; a
gyroscope; an image sensor, such as image sensor 371; and so forth.
In some examples, the at least one wearable sensor may comprise a
wearable image sensor, such as a wearable version of image sensor
371, and the orientation of the at least one wearable sensor with
respect to the wearer may be obtained: by detecting the horizon in
the captured images, by identifying in the captured images a
specific body part of the wearer (such as head, torso, etc.), and
so forth. In some examples, the at least one wearable sensor may
comprise a wearable audio sensor, such as a wearable version of
audio sensor 360, and the orientation of the at least one wearable
sensor with respect to the wearer and/or the mouth of the wearer
may be based on the directional information associated with the
wearer, where the directional information associated with the
wearer may be obtained as described above.
[0085] In some embodiments, the visual data and/or the preprocessed
visual data may be analyzed to obtain spatial orientation
information associated with one or more speakers. For example, the
torso of a speaker may be detected, and the orientation may be
obtained by determining the orientation of the torso. In another
example, the head and/or face of the speaker may be detected, and
the orientation may be obtained by determining the orientation of
the head and/or face. In another example, at least one eye or parts
of at least one eye may be detected in the visual data and/or the
preprocessed visual data, and the orientation may be obtained by
determining the orientation of the speaker gaze, for example using
eye tracking algorithms.
[0086] In some embodiments, identifying audio portions (654) may
comprise analyzing the audio data and/or the preprocessed audio
data to identify one or more portions of the audio data. In some
examples, an identified portion of the audio data may comprise a
continuous part of the audio data or a non-continuous part of the
audio data. In some examples, at least one of the one or more
portions of the audio data may correspond to at least one of: a
silent part of the audio data; a part of the audio data that does
not contain speech; a utterance; a phoneme; a syllable; a morpheme;
a word; a sentence; a conversation; a number of phonemes; a number
of syllables; a number of morphemes; a number of words; a number of
sentences; a number of conversations; a continuous part of the
audio data corresponding to a single speaker; a non-continuous part
of the audio data corresponding to a single speaker; a continuous
part of the audio data corresponding to a group of speakers; a
non-continuous part of the audio data corresponding to a group of
speakers; and so forth.
[0087] In some embodiments, identifying audio portions (654) may
comprise analyzing the audio data and/or the preprocessed audio
data using one or more rules to identify one or more portions of
the audio data. In some examples, at least part of the one or more
rules may be read from memory. In some examples, at least part of
the one or more rules may be preprogrammed manually. In some
examples, at least part of the one or more rules may be the result
of training algorithms, such as machine learning algorithms and/or
deep learning algorithms, on training examples. The training
examples may include examples of data instances, and in some cases,
each data instance may be labeled with a corresponding desired
label and/or result. In some embodiments, the identification of the
one or more portions of the audio data may be based, at least in
part, on the output of one or more neural networks.
[0088] In some embodiments, identifying audio portions (654) may
comprise: analyzing the audio data and/or the preprocessed audio
data to obtain textual information, for example using module 650;
and analyzing of the textual information to identify one or more
portions of the audio data. For example, the textual information
may comprise a transcription of at least part of the audio data.
The textual information may be analyzed in order to identify one or
more portions of the textual information corresponding to at least
one of: part of the textual information that does not contain
meaningful text; a utterance; a phoneme; a syllable; a morpheme; a
word; a sentence; a conversation; a number of phonemes; a number of
syllables; a number of morphemes; a number of words; a number of
sentences; a number of conversations; continuous part of the
textual information corresponding to a single speaker;
non-continuous part of the textual information corresponding to a
single speaker; continuous part of the textual information
corresponding to a group of speakers; non-continuous part of the
textual information corresponding to a group of speakers; and so
forth. One or more portions of the audio data corresponding to the
one or more portions of the textual information may be identified.
In some examples, the textual information may be analyzed using:
natural language processing algorithms, neural networks algorithms,
machine learning algorithms and/or deep learning algorithms, and so
forth.
[0089] In some embodiments, identifying audio portions (654) may
comprise analyzing the audio data and/or the preprocessed audio
data to identify one or more portions of the audio data associated
with a speaker. In some examples, speaker diarization algorithms
may be applied to identify the speaking time of each speaker in the
audio data, therefore identifying portions of the audio data
associated with selected speakers. In some examples, speaker
recognition algorithms may be applied to identify when a specified
speaker is speaking in the audio data, and/or to identify portions
of the audio data associated with selected speakers. In some cases,
a speaker may be identified as the wearer of a wearable apparatus,
such as a wearable version of apparatus 300. One or more portions
of the audio data may be identified as associated with the wearer.
One or more portions of the audio data may be identified as
associated with a speaker other than the wearer. One or more
portions of the audio data may be identified as associated a group
of a plurality of speakers, for example where the group of a
plurality of speakers does not include the wearer.
[0090] In some embodiments, identifying audio portions (654) may
comprise analyzing the audio data and/or the preprocessed audio
data to identify one or more portions of the audio data based, at
least in part, on spatial information associated with the audio
data. In some examples, one or more portions of the audio data
associated with a selected direction and/or selected range of
directions may be identified. For example, the spatial information
may comprise directional information of sound sources associated
with sounds present in the audio data, directional information
associated with speech present in the audio data, and/or
directional information associated with speakers, and the one or
more portions of the audio data that contain sounds and/or speech
associated with a selected direction and/or selected range of
directions may be identified. For example, the audio data may
comprise audio data captured using a wearable apparatus comprising
one or more audio sensors, such as a wearable version of apparatus
300. In such example, the wearer of the wearable apparatus may be
associated with a selected direction and/or selected range of
directions, and one or more portions of the audio data that contain
sounds and/or speech associated with the selected direction and/or
the selected range of directions may be identified.
[0091] In some embodiments, obtaining prosodic information (656)
may comprise analyzing the audio data and/or the preprocessed audio
data to obtain prosodic information. The prosodic information may
be associated with a group of one or more portions of the audio
data and/or with one or more points in time and/or with one or more
points in the audio data. For example, the prosodic information may
be associated with a group of one or more portions of the audio
data that were identified, for example as described above, as
associated with a given speaker, a given conversation, a given
context, and so forth. In some examples, a group of one or more
portions of the audio data and/or a group of one or more portions
of the preprocessed audio data may be analyzed to obtain prosodic
information associated with a group of one or more portions of the
audio data.
[0092] In some embodiments, the prosodic information may comprise
information associated with speech rhythm. For example, duration of
speech sounds may be measured. Some examples of such speech sounds
may include: vowels, consonants, syllables, utterances, and so
forth. In some cases, statistics related to the duration of speech
sounds may be gathered. In some examples, the variance of vowel
duration may be calculated. In some examples, the percentage of
speech time dedicated to one type of speech sounds may be measured.
In some examples, contrasts between durations of neighboring vowels
may be measured.
[0093] In some embodiments, the prosodic information may comprise
information associated with speech tempo. For example, speaking
rate may be measured. For example, articulation rate may be
measured. In some cases, the number of syllables per a unit of time
may be measured, where the unit of time may include and/or exclude
times of pauses, hesitations, and so forth. In some cases, the
number of words per a unit of time may be measured, where the unit
of time may include and/or exclude times of pauses, hesitations,
and so forth. In some cases, statistics related to the rate of
syllables may be gathered. In some cases, statistics related to the
rate of words may be gathered.
[0094] In some embodiments, the prosodic information may comprise
information associated with pitch of the voice. For example, pitch
may be measured at specified times, randomly, continuously, and so
forth. In some cases, statistics related to the pitch may be
gathered. In some cases, pitch may be measured at different
segments of speech, and statistics related to the pitch may be
gathered for each type of segment separately. In some cases, the
average speaking pitch over a time period may be calculated. In
some cases, the minimal and/or maximal speaking pitch in a time
period may be found.
[0095] In some embodiments, the prosodic information may comprise
information associated with loudness of the voice. For example, the
loudness may be measured as the intensity of the voice. For
example, loudness may be measured at specified times, randomly,
continuously, and so forth. In some cases, statistics related to
the loudness may be gathered. In some cases, loudness may be
measured at different segments of speech, and statistics related to
the loudness may be gathered for each type of segment separately.
In some cases, the average speaking loudness over a time period may
be calculated. In some cases, the minimal and/or maximal speaking
loudness in a time period may be found.
[0096] In some embodiments, the prosodic information may comprise
information associated with intonation of the voice. For example,
the pitch of the voice may be analyzed to identify rising and
falling intonations. In another example, rising intonation, falling
intonation, dipping intonation, and/or peaking intonation may be
identified. For example, intonation may be identified at specified
times, randomly, continuously, and so forth. In some cases,
statistics related to the intonation may be gathered.
[0097] In some embodiments, the prosodic information may comprise
information associated with a linguistic tone associated with a
portion of the audio data. For example, the usage of pitch to
distinguish and/or inflect words, to express emotional and/or
paralinguistic information, to convey emphasis, contrast, and so
forth, may be identified. Some examples of linguistic tone may
include: abashed, abrasive, abusive, accepting, acquiescent,
admiring, adoring, affectionate, aggravated, aghast, allusive,
amused, angry, anxious, apologetic, appreciative, apprehensive,
approving, arch, ardent, argumentative, artificial, ashamed,
audacious, authoritative, awe-struck, bantering, begrudging,
bemused, benevolent, biting, bitter, blithe, boastful, bored,
bristling, brusque, calm, candid, caring, caustic, cavalier,
cheerful, childish, child-like, clipped, cold, compassionate,
complimentary, condemning, condescending, confident, contemptuous,
conversational, coy, critical, curt, cutting, cynical,
denunciatory, despairing, detached, didactic, disappointed,
disbelieving, disconcerted, discouraged, disdainful, disgusted,
disinterested, disparaging, disrespectful, distracted, doubtful,
dramatic, dreamy, dry, ecstatic, embarrassed, energetic, entranced,
enthusiastic, eulogistic, excited, exhilarated, exultant,
facetious, fanciful, fearful, flippant, fond, forceful, friendly,
frightened, ghoulish, giddy, gleeful, glum, grim, guarded, guilty,
happy, harsh, hateful, haughty, heavy-hearted, hollow, horrified,
humorous, hypercritical, indifferent, indignant, indulgent,
inflammatory, insulting, ironic, irreverent, irritated, joking,
joyful, languorous, languid, laudatory, light-hearted, lingering,
loving, manipulative, marveling, melancholy, mistrustful, mocking,
mysterious, naive, negative, neutral, nostalgic, objective,
passionate, patronizing, peaceful, pessimistic, pitiful, playful,
poignant, positive, pragmatic, proud, provocative, questioning,
rallying, reflective, reminiscing, reproachful, resigned,
respectful, restrained, reticent, reverent, ridiculing, romantic,
rueful, sad, sarcastic, sardonic, satiric, satisfied, seductive,
self-critical, self-dramatizing, self-justifying, self-mocking,
self-pitying, self-satisfied, sentimental, serious, severe, sharp,
shocked, silly, sly, smug, solemn, somber, stentorian, stern,
straightforward, strident, stunned, subdued, surprised, swaggering,
sweet, sympathetic, taunting, teasing, tense, thoughtful,
threatening, tired, touchy, trenchant, uncertain, understated,
upset, urgent, vexed, vibrant, wary, whimsical, withering, wry,
zealous, and so forth.
[0098] In some embodiments, the prosodic information may comprise
information associated with stress of the voice. For example,
loudness of the voice and/or vowels length may be analyzed to
identify an emphasis given to a specific syllable. In another
example, loudness of the voice and pitch may be analyzed to
identify emphasis on specific words, phrases, sentences, and so
forth. In an additional example, loudness, vowel length,
articulation of vowels, pitch, and so forth may be analyzed to
identify emphasis associated with a specific time of speaking, with
specific portions of speech, and so forth.
[0099] In some embodiments, the prosodic information may comprise
information associated with pauses. For example, length of pauses
may be measured. In some cases, statistics related to the length of
pauses may be gathered.
[0100] In some embodiments, the prosodic information may comprise
information associated with timbre of the voice. For example, voice
brightness may be identified. As another example, formant structure
associated with the pronunciation of the different sounds may be
identified. In some embodiments, the prosodic information may
comprise information associated with accent. For example, the type
of accent may be identified. In some embodiments, the prosodic
information may comprise an identification of flatness level of a
voice.
[0101] In some embodiments, obtaining prosodic information (656)
may comprise analyzing the audio data and/or the preprocessed audio
data using one or more rules to obtain prosodic information. In
some examples, at least part of the one or more rules may be read
from memory. In some examples, at least part of the one or more
rules may be received from an external device, for example using a
communication device. In some examples, at least part of the one or
more rules may be preprogrammed manually. In some examples, at
least part of the one or more rules may be the result of training
algorithms, such as machine learning algorithms and/or deep
learning algorithms, on training examples. The training examples
may include examples of data instances, and in some cases, each
data instance may be labeled with a corresponding desired label
and/or result. For example, the training examples may include audio
samples that contain speech, and be labeled according to the
prosodic properties of the contained speech. In some embodiments,
the identification of the prosodic information may be based, at
least in part, on the output of one or more neural networks.
[0102] In some embodiments, identifying conversations (658) may
comprise obtaining an indication that two or more speakers are
engaged in conversation. For example, speaker diarization
information may be obtained, for example by using a speaker
diarization algorithm. The speaker diarization information may be
analyzed in order to identify which speakers are engaged in
conversation at what time, for example by detecting a sequence in
time in which two or more speakers talk in turns. In another
example, clustering algorithms may be used to analyze the speaker
diarization information and divide the speaker diarization
information to conversations. In another example, the speaker
diarization information may be divided when no activity is recorder
in the speaker diarization information for duration longer than a
selected threshold.
[0103] In some embodiments, identifying conversations (658) may
comprise analyzing the audio data and/or the preprocessed audio
data to identify a conversation in the audio data. Some examples of
such analysis methods may include: the application of speaker
diarization algorithms in order to obtain speaker diarization
information, and analyzing the speaker diarization information as
described above; the usage of neural networks trained to detect
conversations within audio data, where the input to the neural
networks may comprise the audio data and/or the preprocessed audio
data; analyzing the audio data and/or the preprocessed audio data
to obtain textual information, for example using module 650, and
analyzing of the textual information to identify conversations, for
example using textual conversation identification algorithms; and
so forth. In some examples, speakers taking part in that
conversation may be identified, for example using speaker
recognition algorithms. Some examples of such speaker recognition
algorithms may include: pattern recognition algorithms; hidden
Markov models based algorithms; mixture of Gaussians based
algorithms; pattern matching based algorithms; neural networks
based algorithms; quantization based algorithms; machine learning
and/or deep learning based algorithms; and so forth.
[0104] In some embodiments, identifying conversations (658) may
comprise analyzing the visual data and/or the preprocessed visual
data to identify a conversation involving two or more speakers
visible in the visual data, and possibly in order to identify the
speakers taking part in the conversation, for example using face
recognition algorithms. Some examples of such analysis may
comprise: usage of action recognition algorithms; usage of lips
reading algorithms; and so forth.
[0105] In some embodiments, identifying conversations (658) may
comprise analyzing information coming from variety of sensors, for
example identifying conversations based on an analysis of audio
data and visual data.
[0106] In some embodiments, identifying speakers (660) may comprise
obtaining identifying information associated with one or more
speakers. In some examples, identifying speakers (660) may identify
the name of one or more speakers, for example by accessing a
database that comprises names and identifying audible and/or visual
features. In some examples, identifying speakers (660) may identify
demographic information associated with one or more speakers, such
as age, sex, and so forth. In some embodiments, identifying
speakers (660) may comprise analyzing the input data using one or
more rules to determine demographic information associated with one
or more speakers, such as age, sex, and so forth. In some examples,
at least part of the one or more rules may be read from memory. In
some examples, at least part of the one or more rules may be
preprogrammed manually. In some examples, at least part of the one
or more rules may be the result of training algorithms, such as
machine learning algorithms and/or deep learning algorithms, on
training examples. The training examples may include examples of
data instances, and in some cases, each data instance may be
labeled with a corresponding desired label and/or result. For
example, the training examples may include audio samples that
contain speech, and be labeled according to the age and/or sex of
the speaker. In another example, the training examples may include
images that contain faces, and be labeled according to the age
and/or sex of the faces. In some embodiments, the determining
demographic information may be based, at least in part, on the
output of one or more neural networks.
[0107] In some embodiments, identifying speakers (660) may comprise
analyzing the audio data and/or the preprocessed audio data to
identify one or more speakers and/or to identify information
associated with one or more speakers, for example using speaker
recognition algorithms. Some examples of such speaker recognition
algorithms may include: pattern recognition algorithms; hidden
Markov models based algorithms; mixture of Gaussians based
algorithms; pattern matching based algorithms; neural networks
based algorithms; quantization based algorithms; machine learning
and/or deep learning based algorithms; and so forth.
[0108] In some embodiments, identifying speakers (660) may comprise
analyzing the visual data and/or the preprocessed visual data to
detect one or more speakers and/or to identify one or more speakers
and/or to identify information associated with one or more
speakers, for example using lips movement detection algorithms,
face recognition algorithms, and so forth.
[0109] In some embodiments, measuring lengths (664) may comprise
obtaining a measurement associated with the length of one or more
segments of the audio data, or a measurement associated with the
length of information associated with one or more segments of the
audio data, for example by analyzing the audio data and/or the
preprocessed audio data.
[0110] In some embodiments, measuring lengths (664) may comprise
obtaining a measurement associated with the length of time of at
least one of the following segments of the audio data: the entire
audio data; a silent part of the audio data; a part of the audio
data that does not contain speech; a part of the audio data that
contains speech; a utterance; a phoneme; a syllable; a morpheme; a
word; a sentence; a question; a conversation; a number of phonemes;
a number of syllables; a number of morphemes; a number of words; a
number of sentences; a number of conversations; a continuous part
of the audio data; a non-continuous part of the audio data; a
continuous part of the audio data corresponding to a single
speaker; a non-continuous part of the audio data corresponding to a
single speaker; a continuous part of the audio data corresponding
to a group of speakers; a non-continuous part of the audio data
corresponding to a group of speakers; any combination of the above;
and so forth.
[0111] In some embodiments, measuring lengths (664) may comprise
obtaining a measurement associated with the length of a segment of
the audio data, or a measurement associated with the length of
information associated with a segment of the audio data, may be
measured by counting the number of objects contained within the
segment, or within the information associated with the segment.
Some examples of such objects may include: a phoneme; a syllable; a
morpheme; a word; a utterance; a sentence; a question; a
conversation; and so forth. For example, a length of syllable may
be measured by counting the number of phonemes contained within the
syllable. In another example, a length of a morpheme may be
measured by counting the number of phonemes or syllables contained
within the morpheme. In an additional example, the length of a word
may be measured by counting the number of phonemes, syllables, or
morphemes contained within the word. In another example, the length
of a utterance, a sentence or a question may be measured by
counting the number of phonemes, syllables, morphemes or words
contained within the utterance, the sentence, or the question. In
an additional example, the length of a conversation or a part of a
conversation may be measured by counting the number of phonemes,
syllables, morphemes, words, utterances, sentences, or questions
contained within the conversation or the part of a conversation. In
another example, the length of a part of the audio data
corresponding to a single speaker may be measured by counting the
number of phonemes, syllables, morphemes, words, utterances,
sentences, questions or conversations contained within the part of
the audio data corresponding to a single speaker.
[0112] In some embodiments, measuring lengths (664) may comprise
analyzing the audio data and/or the preprocessed audio data and/or
information associated with a segment of the audio data using one
or more rules. In some examples, at least part of the one or more
rules may be read from memory. In some examples, at least part of
the one or more rules may be preprogrammed manually. In some
examples, at least part of the one or more rules may be the result
of training algorithms, such as machine learning algorithms and/or
deep learning algorithms, on training examples. The training
examples may include examples of data instances, and in some cases,
each data instance may be labeled with a corresponding desired
label and/or result. In some embodiments, measuring lengths (664)
may comprise the usage of one or more neural networks, and the
obtained measurements may be based, at least in part, on the output
of the one or more neural networks. In some embodiments, measuring
lengths (664) may comprise analyzing the audio data and/or the
preprocessed audio data and/or information associated with a
segment of the audio data using one or more regression models.
[0113] In some embodiments, measuring lengths (664) may comprise
analyzing the audio data and/or the preprocessed audio data to
obtain textual information, for example using module 650; and
analyzing of the textual information to obtain a measurement
associated with the length of one or more segments of the audio
data, or a measurement associated with the length of information
associated with one or more segments of the audio data. For
example, the textual information may comprise a transcription of at
least part of the audio data. The transcription may be analyzed in
order to identify one or more objects, such as: letters; syllables;
morphemes; words; utterances; sentences; questions; conversations;
and so forth. The measurement may be based, at least in part, on
the number of objects identified within a segment of the
transcription, on the number of objects associated with a segment
of the audio data, and so forth.
[0114] In some examples, the measurement associated with the length
of one or more segments of the audio data, and/or the measurement
associated with the length of information associated with one or
more segments of the audio data, may comprise information related
to at least one of: the mean length; the variance of the length;
the distribution of lengths; statistics related to the length;
histogram of lengths; and so forth.
[0115] In some embodiments, identifying context (680) may comprise
obtaining context information. For example, identifying context
(680) may comprise analyzing input data using one or more rules to
identify context information and/or parameters of the context
information. For example, the input data may include one or more
of: audio data; preprocessed audio data; textual information;
visual data; preprocessed visual data; physiological data;
preprocessed physiological data; positioning data; preprocessed
positioning data; motion data; preprocessed motion data; user
input; and so forth. In some examples, at least part of the one or
more rules may be read from memory. In some examples, at least part
of the one or more rules may be preprogrammed manually. In some
examples, at least part of the one or more rules may be the result
of training algorithms, such as machine learning algorithms and/or
deep learning algorithms, on training examples. The training
examples may include examples of input data instances, and in some
cases, each input data instance may be labeled with a corresponding
desired label and/or result, such as desired context information
and/or desired parameters of the context information. In some
embodiments, the identification of the context information and/or
parameters of the context information may be based, at least in
part, on the output of one or more neural networks. In some
embodiments, prototypes may be used, the most similar prototype to
the input data may be selected, and the context information and/or
parameters of the context information may be based, at least in
part, on the selected prototype. For example, prototypes may be
generated manually. In another example, prototypes may be generated
by clustering input data examples, and the centroids of the
clusters may be used as prototypes.
[0116] In some embodiments, identifying context (680) may comprise
analyzing the audio data and/or the preprocessed audio data to
identify at least part of the context information. In some
examples, identifying context (680) may comprise: analyzing the
audio data and/or the preprocessed audio data to obtain textual
information, for example using module 650; and analyzing of the
textual information to identify context information and/or
parameters of the context information. For example, the textual
information may comprise a transcription of at least part of the
audio data, and natural language processing algorithms may be used
to determine context information and/or parameters of the context
information. In another example, the textual information may
comprise keywords, and the context information and/or parameters of
the context information may be determined based on the keywords. In
some examples, identifying context (680) may comprise determining
the context information and/or parameters of the context
information based on prosodic information, such as the prosodic
information obtained using module 656.
[0117] In some embodiments, identifying context (680) may comprise
analyzing the visual data and/or the preprocessed visual data to
identify at least part of the context information. For example, the
visual data and/or the preprocessed visual data may be analyzed to
identify scene information, for example using visual scene
recognition algorithms, and the context information and/or
parameters of the context information may be based, at least in
part, on the scene information. For example, the visual data and/or
the preprocessed visual data may be analyzed to identify one or
more persons in the environment and/or demographic information
related to the one or more persons, for example using face
detection and/or face recognition algorithms and/or module 660, and
the context information and/or parameters of the context
information may be based, at least in part, on the identity of the
one or more persons and/or the demographic information related to
the one or more persons. For example, the visual data and/or the
preprocessed visual data may be analyzed to detect one or more
objects in the environment and/or information related to the one or
more objects, for example using object detection algorithms, and
the context information and/or parameters of the context
information may be based, at least in part, on the detected one or
more objects and/or the information related to the one or more
objects. For example, the visual data and/or the preprocessed
visual data may be analyzed to detect one or more activities in the
environment and/or information related to the one or more
activities, for example using activity detection algorithms, and
the context information and/or parameters of the context
information may be based, at least in part, on the detected one or
more activities and/or the information related to the one or more
activities. For example, the visual data and/or the preprocessed
visual data may be analyzed to identify text in the environment,
for example using optical character recognition algorithms, and the
context information and/or parameters of the context information
may be based, at least in part, on the identified text.
[0118] In some embodiments, identifying context (680) may comprise
determining the context information and/or parameters of the
context information based, at least in part, on spatial
information, such as the spatial information obtained using module
652. In some embodiments, identifying context (680) may comprise
determining the context information and/or parameters of the
context information based, at least in part, on conversations or
information related to conversations, such as the conversations
identified using module 658. In some examples, context information
and/or parameters of the context information may be based, at least
in part, on properties of the identified conversations, such as the
length of the conversation, the number of participants in the
conversation, the identity of one or more participants, the topics
of the conversation, keywords from the conversation, and so forth.
In some embodiments, identifying context (680) may comprise
determining the context information and/or parameters of the
context information based, at least in part, on identifying
information associated with one or more speakers, such as
identifying information associated with one or more speakers
obtained using module 660.
[0119] In some embodiments, providing feedbacks (690) may comprise
providing one or more feedbacks to one or more users. In some
examples, feedback may be provided upon a detection of: an event;
an event that matches certain criterions; an event associated with
properties that match certain criterions; an assessment result that
match certain criterions; an item or object that matches certain
criterions; an item or object associated with properties that
matches certain criterions; and so forth. In some examples, the
nature and/or content of the feedback may depend on: the detected
event; the identified properties of the detected event; the
detected item; the identified properties of the detected item; the
detected object; the identified properties of the detected object;
and so forth. In some examples, such events, items and/or objects
may be detected by a processing unit, such as processing units
330.
[0120] In some embodiments, after providing a first feedback, one
or more additional events may be identified. In such cases,
providing feedbacks (690) may comprise providing additional
feedbacks upon the detection of the additional events. For example,
the additional feedbacks may be provided in a similar fashion to
the first feedback. In some examples, the system may avoid
providing additional similar feedbacks for selected time duration.
In some examples, the additional feedback may be identical to the
previous feedback. In some examples, the additional feedback may
differ from the previous feedback, for example by being of
increased intensity, by mentioning the previous feedback, and so
forth.
[0121] In some embodiments, providing feedbacks (690) may comprise
providing one or more feedbacks to one or more users. In some
examples, feedbacks may be provided upon the identification of a
trigger. In some examples, the nature of the feedback may depend on
information associated with the trigger, such as the type of the
trigger, properties of the identified trigger, and so forth.
Examples of such triggers may include: voice commands, such as
voice commands captured using audio sensors 360; press of a button;
hand gestures, such as hand gestures captured using image sensors
371; and so forth. In some examples, such triggers may be
identified by a processing unit, such as processing units 330.
[0122] In some embodiments, providing feedbacks (690) may comprise
providing one or more feedbacks as a: visual output, for example
using visual outputting units 352; audio output, for example using
audio output units 351; tactile output, for example using tactile
outputting units 353; electric current output; any combination of
the above; and so forth. In some examples, the amount of feedbacks,
the events triggering feedbacks, the content of the feedbacks, the
nature of the feedbacks, etc., may be controlled by configuration.
The feedbacks may be provided: by the apparatus detecting the
events; through another apparatus; and so forth. In some examples,
the feedbacks may be provided by a wearable apparatus, such as a
wearable version of wearable apparatus 300. The feedbacks provided
by the wearable apparatus may be provided to: the wearer of the
wearable apparatus; one or more caregivers of the wearer of the
wearable apparatus; any combination of the above; and so forth.
[0123] In some embodiments, providing reports (692) may comprise
generating and/or providing one or more reports to one or more
users. For example, information may be aggregated, including
information related to: detected events; assessment results;
identified objects; identified items; and so forth. The information
may be aggregated by a processing unit, such as processing units
330. The aggregated information may be stored in memory, such as
memory units 320, shared memory modules 520, and so forth. Some
examples of such aggregated information may include: a log of
detected events, objects, and/or items, possibly together
identified properties of the detected events, objects and/or items;
statistics related to the detected events, objects, and/or items;
statistics related to the identified properties of the detected
events, objects, and/or items; and so forth. In some embodiments,
providing reports (692) may comprise generating and/or providing
one or more reports based on the aggregated information. In some
examples, the report may comprise: all or part of the aggregated
information; a summary of the aggregated information; information
derived from the aggregated information; statistics based on the
aggregated information; and so forth. In some examples, the reports
may include a comparison of the aggregated information to: past
information, such as past performance information; goals; normal
range values; and so forth.
[0124] In some embodiments, providing reports (692) may comprise
providing one or more reports: in a printed form, for example using
one or more printers; audibly read, for example using audio
outputting units 351; visually displayed, for example using visual
outputting units 352; and so forth. In some examples, the reports
may be provided by or in conjunction with a wearable apparatus,
such as a wearable version of apparatus 300. The generated reports
may be provided to: the wearer of the wearable apparatus; one or
more caregivers of the wearer of the wearable apparatus; any
combination of the above; and so forth.
[0125] FIG. 7 illustrates an example of process 700 for analyzing
audio to obtain language register information. In some examples,
process 700, as well as all individual steps therein, may be
performed by various aspects of: apparatus 300; server 400; cloud
platform 500; computational node 510; and so forth. For example,
process 700 may be performed by processing units 330, executing
software instructions stored within memory units 320 and/or within
shared memory modules 520. In this example, process 700 may
comprise: obtaining audio data (using module 612); and analyzing
audio data to obtain language register information (Step 720). In
some implementations, process 700 may comprise one or more
additional steps, while some of the steps listed above may be
modified or excluded. For example, process 700 may also comprise
providing feedbacks (using module 690) and/or providing reports
(using module 692). In some implementations, Step 720 may be
executed after and/or simultaneously with module 612. Examples of
possible execution manners of process 700 may include: continuous
execution, returning to the beginning of the process and/or to Step
720 once the process normal execution ends; periodically execution,
executing the process at selected times; execution upon the
detection of a trigger, where examples of such trigger may include
a trigger from a user, a trigger from another process, etc.; any
combination of the above; and so forth.
[0126] In some embodiments, analyzing audio data to obtain language
register information (Step 720) may comprise analyzing the audio
data and/or the preprocessed audio data to obtain language register
information. In some examples, the language register information
may be associated with: the entire audio data; with the one or more
portions of the audio data, such as one or more portions of the
audio identified by module 654; with one or more portions of the
audio data associated with a speaker; with one or more portions of
the audio data associated with the wearer of a wearable apparatus;
with one or more portions of the audio data associated with a group
of speakers; with one or more portions of the audio data associated
with speakers engaged in conversation with the wearer of the
wearable apparatus; and so forth. In some examples, multiple
language register information records may be obtained for multiple
groups of portions of the audio data. In some examples, a
conversation may be identified, for example using module 658, and
language register information may be obtained for different
speakers engaged in the identified conversation. In some examples,
the audio data and/or the preprocessed audio data may be analyzed
in order to determine a context associated with the usage of the
language register, for example using module 680.
[0127] In some embodiments, analyzing audio data to obtain language
register information (Step 720) may comprise analyzing the audio
data and/or the preprocessed audio data to determine if and/or when
the language register is: an intimate register, a casual register,
a formal register, a consultative register, a bench level register,
a dialect register, a facetious register, an in house register, an
ironic register, a neutral register, a slang register, a taboo
register, a technical register, a vulgar register, and so forth. In
some examples, the audio data and/or the preprocessed audio data
may be analyzed in order to determine the language register type
according to ISO 12620 standard.
[0128] In some embodiments, analyzing audio data to obtain language
register information (Step 720) may comprise: analyzing the audio
data and/or the preprocessed audio data to obtain textual
information, for example using module 650; and analyzing the
obtained textual information to obtain language register
information. For example, the textual information may be analyzed
using: natural language processing algorithms, neural networks
algorithms, and so forth. For example, the textual information
and/or portions of the textual information may be classified using
one or more classification rules to determine the language register
and/or to determine if the language register is socially acceptable
in the current context. For example, the textual information
corresponding to a portion of the audio data may be represented in
as a bag of words vector, and the bag of words vector may be
classified to determine language register, for example using a
k-nearest neighbors algorithm, using a nearest centroid classifier
algorithm, and so forth.
[0129] In some embodiments, analyzing audio data to obtain language
register information (Step 720) may comprise analyzing the audio
data and/or the preprocessed audio data using one or more rules to
obtain the language register information and/or to determine if the
language register is socially acceptable in the current context. In
some examples, at least part of the one or more rules may be read
from memory. In some examples, at least part of the one or more
rules may be received from an external device, for example using a
communication device. In some examples, at least part of the one or
more rules may be preprogrammed manually. In some examples, at
least part of the one or more rules may be the result of training
algorithms, such as machine learning algorithms and/or deep
learning algorithms, on training examples. The training examples
may include examples of data instances, and in some cases, each
data instance may be labeled with a corresponding desired label
and/or result. For example, the training examples may include audio
clips and may be labeled according to the type of the language
register of the speech included in the clips. In an additional
example, the training examples may include audio clips that include
conversations, and the training examples may be labeled based on
the social acceptability of the language register of the speakers
engaged in the conversation. In some examples, the one or more
rules may be based, at least in part, on the output of one or more
neural networks.
[0130] In some embodiments, feedback may be provided to a user,
such as a wearer of the wearable apparatus, based on the language
register information, for example using module 690. In some cases,
the feedback may be provided upon the detection of language
register information that meets a certain criteria, for example
that the type of the language register is of a selected language
register types. For example, feedback may be provided: when the
language register information associated with a specific speaker
meets a certain criteria; when the language register information
associated with the wearer meets a certain criteria; when the
language register information associated with a first speaker meets
a certain criteria and the language register information associated
with a second speaker meets another criteria; when the language
register information associated with a first speaker meets a
certain criteria and the language register information associated
with a second speaker that is engaged in conversation with the
first speaker meets another criteria; when the language register
information associated with the wearer meets a certain criteria and
the language register information associated with a second speaker
meets another criteria; when the language register information
associated with the wearer meets a certain criteria and the
language register information associated with a speaker that is
engaged in conversation with the wearer meets another criteria;
when the language register information associated with a speaker
that is engaged in conversation with the wearer meets a certain
criteria; when the language register of the wearer is not socially
acceptable; any combination of the above; and so forth. In some
cases, the nature of the feedback may depend on the language
register information. For example, the feedback intensity may be
control based on the language register information. In an
additional example, the feedback may contain visual text and/or
audible speech, and the content of the visual text and/or audible
speech may be selected based on the language register information.
In some examples, the feedback may inform the wearer about the
language register of a person that is engaged in conversation with
the wearer.
[0131] In some embodiments, information related to the obtained
language register information may be aggregated. For example,
information related to the language register information associated
with audio data captured at different times may be aggregated. The
aggregated information may be stored in memory, for example in a
log file, in a database, in a data-structure, in a container
data-structure, and so forth. In some examples, the aggregated
information may comprise one or more of: records of the language
register information; information related to the speaker associated
with the language register information; audio recordings of at
least part of the audio data associated with the language register
information; and so forth. In some examples, reports based on the
aggregated information may be generated and/or provided to one or
more users, for example using Step 692. For example, a report may
comprise statistics related to the language register information,
to the language register of a specific speaker, to the language
register of the wearer of the wearable apparatus, to the language
register information associated with specific context, and so
forth. For example, a report may comprise times at which specific
language register information were detected and/or statistics
related to these times. In some examples, the reports may include a
comparison of the aggregated information to: past performances,
goals, normal range values, and so forth.
[0132] In some examples, one language register information record
may be assessed according to other language register information
records. For example, language register information record
corresponding to audio data from one point in time may be assessed
according to language register information record corresponding to
audio data from another point in time. For example, language
register information record corresponding to one speaker may be
assessed according to language register information record
corresponding to another speaker, for example when the two speakers
are engaged in conversation, when one of the speakers is a wearer
of a wearable apparatus, and so forth. For example, it may be
determined if a language register information of a speaker is
socially acceptable in a conversation given the language register
information of other speakers engaged in the conversation, for
example by checking an entry corresponding to the two language
register types in a socially acceptable combinations matrix. In
some examples, information regarding this assessment may be
aggregated. In some examples, information, feedbacks and reports
based on the assessment and/or the aggregated information may be
provided to a user, for example as described above.
[0133] It will also be understood that the system according to the
invention may be a suitably programmed computer, the computer
including at least a processing unit and a memory unit. For
example, the computer program can be loaded onto the memory unit
and can be executed by the processing unit. Likewise, the invention
contemplates a computer program being readable by a computer for
executing the method of the invention. The invention further
contemplates a machine-readable memory tangibly embodying a program
of instructions executable by the machine for executing the method
of the invention.
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