U.S. patent application number 16/399122 was filed with the patent office on 2020-11-05 for audio stream processing for distributed device meeting.
The applicant listed for this patent is Microsoft Technology Licensing, LLC. Invention is credited to Zhuo Chen, Dimitrios Basile Dimitriadis, William Isaac Hinthorn, Xuedong Huang, Lijuan Qin, Andreas Stolcke, Takuya Yoshioka, Nanshan Zeng.
Application Number | 20200351603 16/399122 |
Document ID | / |
Family ID | 1000005162542 |
Filed Date | 2020-11-05 |
![](/patent/app/20200351603/US20200351603A1-20201105-D00000.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00001.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00002.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00003.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00004.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00005.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00006.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00007.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00008.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00009.png)
![](/patent/app/20200351603/US20200351603A1-20201105-D00010.png)
View All Diagrams
United States Patent
Application |
20200351603 |
Kind Code |
A1 |
Hinthorn; William Isaac ; et
al. |
November 5, 2020 |
Audio Stream Processing for Distributed Device Meeting
Abstract
A computer implemented method includes receiving multiple
channels of audio from three or more microphones detecting speech
from a meeting of multiple users, localizing speech sources to
determine an approximate direction of arrival of speech from a
user, using a speech unmixing model to select two channels
corresponding to a primary and a secondary microphone, and sending
the two selected channels to a meeting server for generation of a
speaker attributed meeting transcript.
Inventors: |
Hinthorn; William Isaac;
(Seattle, WA) ; Qin; Lijuan; (Redmond, WA)
; Zeng; Nanshan; (Bellevue, WA) ; Dimitriadis;
Dimitrios Basile; (Bellevue, WA) ; Chen; Zhuo;
(Woodinville, WA) ; Stolcke; Andreas; (Berkeley,
CA) ; Yoshioka; Takuya; (Bellevue, WA) ;
Huang; Xuedong; (Bellevue, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Microsoft Technology Licensing, LLC |
Redmond |
WA |
US |
|
|
Family ID: |
1000005162542 |
Appl. No.: |
16/399122 |
Filed: |
April 30, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 21/4302 20130101;
H04N 21/42203 20130101; H04S 2400/01 20130101; H04R 5/04 20130101;
H04R 3/005 20130101; H04S 3/002 20130101 |
International
Class: |
H04S 3/00 20060101
H04S003/00; H04R 3/00 20060101 H04R003/00; H04R 5/04 20060101
H04R005/04; H04N 21/43 20060101 H04N021/43; H04N 21/422 20060101
H04N021/422 |
Claims
1. A computer implemented method comprising: receiving multiple
channels of audio from three or more microphones capturing speech
from a meeting of multiple users; localizing speech sources to
determine an approximate direction of arrival of speech from a
user; using a speech unmixing model to select two channels
corresponding to a primary and a secondary microphone; and sending
the two selected channels to a meeting server for generation of a
speaker attributed meeting transcript.
2. The method of claim 1 wherein the microphones are supported by a
device in a fixed configuration.
3. The method of claim 2 wherein the fixed configuration includes a
camera having a field of view configured to include the multiple
users.
4. The method of claim 3 wherein localizing speech sources
comprises using a model trained on channels of audio and video from
a device having a same fixed configuration.
5. The method of claim 1 wherein the microphones are associated
with multiple distributed devices.
6. The method of claim 5 wherein the distributed devices comprise
wireless devices respectively associated with the multiple
users.
7. The method of claim 6 wherein at least one of the distributed
devices includes a camera providing a video stream of at least one
of the users, wherein the distributed device with the camera
provides an audio stream synchronized with the video stream such
that synchronization of the audio stream with other audio streams
provides synchronization information for synchronizing the video
stream.
8. The method of claim 1 wherein the microphones include
microphones supported in a fixed configuration and microphones
associated with distributed devices associated with users.
9. The method of claim 1 wherein the method is performed by an
ambient device supporting the microphones in a fixed position or a
client-side device receiving the multiple channels of audio.
10. The method of claim 9 wherein the speech unmixing model is
executing on the client-side device.
11. The method of claim 9 wherein localizing speech sources is
performed on a client-side device.
12. A machine-readable storage device having instructions for
execution by a processor of a machine to cause the processor to
perform operations to perform a method, the operations comprising:
receiving multiple channels of audio from three or more microphones
detecting speech from a meeting of multiple users; localizing
speech sources to determine an approximate direction of arrival of
speech from a user; using a speech unmixing model to select two
channels corresponding to a primary and a secondary microphone; and
sending the two selected channels to a meeting server for
generation of a speaker attributed meeting transcript.
13. The device of claim 12 wherein the microphones are supported by
a device in a fixed configuration.
14. The device of claim 13 wherein the fixed configuration includes
a camera having a field of view configured to include the multiple
users.
15. The device of claim 14 wherein the fixed configuration includes
a camera having a field of view configured to include the multiple
users and wherein localizing speech sources comprises using a model
trained on channels of audio and video from a device having a same
fixed configuration.
16. The device of claim 12 wherein the microphones are associated
with multiple distributed devices, wherein the distributed devices
comprise wireless devices respectively associated with the multiple
users, and wherein at least one of the distributed devices includes
a camera providing a video stream of at least one of the users,
wherein the distributed device with the camera provides an audio
stream synchronized with the video stream such that synchronization
of the audio stream with other audio streams provides
synchronization information for synchronizing the video stream.
17. The device of claim 12 wherein the microphones include
microphones supported in a fixed configuration and microphones
associated with distributed devices associated with users.
18. The device of claim 12 wherein localizing speech sources is
performed on a client-side device.
19. A device comprising: a processor; and a memory device coupled
to the processor and having a program stored thereon for execution
by the processor to perform operations comprising: receiving
multiple channels of audio from three or more microphones detecting
speech from a meeting of multiple users; localizing speech sources
to determine an approximate direction of arrival of speech from a
user; using a speech unmixing model to select two channels
corresponding to a primary and a secondary microphone; and sending
the two selected channels to a meeting server for generation of a
speaker-attributed meeting transcript.
20. The device of claim 19 wherein the microphones are supported by
a device in a fixed configuration that includes a camera having a
field of view configured to include the multiple users and a camera
having a field of view configured to include the multiple users and
wherein localizing speech sources comprises using a model trained
on channels of audio and video from a device having a same fixed
configuration.
Description
BACKGROUND
[0001] Meetings that are planned in advance may make use of one or
more conferencing tools that are set up in advance of the meeting
or at the start of a meeting to record the conversation and
generate a speaker attributed transcript. Such existing
conferencing tools may include a device having a. plurality of
fixed speakers on different sides of the device that sits on a
conference table. The device may have a tower- or cone-like shape
and may have a video camera that can be used to identify and track
people in the meeting. Speech to text algorithms may be used to
create the transcript. Audio beamforming may be used in conjunction
with the known locations of the fixed speakers along with video of
attendees to attribute speech in the transcript.
SUMMARY
[0002] A computer implemented method includes receiving multiple
channels of audio from three or more microphones detecting speech
from a meeting of multiple users, localizing speech sources to
determine an approximate direction of arrival of speech from a
user, using a speech unmixing model to select two channels
corresponding to a primary and a secondary microphone, and sending
the two selected channels to a meeting server for generation of an
speaker-attributed meeting transcript.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a perspective view of a meeting between multiple
users according to an example embodiment.
[0004] FIG. 2 is a block diagram of a user device for use in
meetings according to an example embodiment.
[0005] FIG. 3 is a flowchart illustrating a method of initiating an
intelligent meeting between two users with associated distributed
devices according to an example embodiment.
[0006] FIG. 4 is a flowchart illustrating a method of adding
distributed devices to an intelligent meeting by the use of a
conference code according to an example embodiment.
[0007] FIG. 5 is a computer implemented method of adding further
devices to an intelligent meeting according to an example
embodiment.
[0008] FIG. 6 is a flowchart illustrating a computer implemented
method of detecting that an ad-hoc meeting is occurring according
to an example embodiment.
[0009] FIG. 7 is a flowchart illustrating a computer implemented
method of removing audio channels from user devices and other
devices in response to users leaving a meeting according to an
example embodiment.
[0010] FIG. 8 is a flowchart illustrating a computer implemented
method of authenticating a device for adding an audio stream from
the device to audio channels being processed by a meeting server
instance according to an example embodiment.
[0011] FIG. 9 is a high-level block flow diagram of a system for
generating a transcript for a meeting between multiple users
according to an example embodiment.
[0012] FIG. 10 is a detailed block flow diagram illustrating
distributed meeting server processing of information including
audio streams from distributed devices according to an example
embodiment.
[0013] FIG. 11 is a flowchart illustrating a computer implemented
method of synchronizing multiple audio channels received from
multiple distributed devices during an intelligent meeting
according to an example embodiment.
[0014] FIG. 12 is a flowchart illustrating a computer implemented
method of separating overlapped speech in a distributed device
intelligent meeting according to an example embodiment.
[0015] FIG. 13 is a flowchart illustrating a computer implemented
method 13 of fusing audio streams at multiple selected points
during processing according to an example embodiment.
[0016] FIGS. 14A and 14B illustrate an example ambient capture
device according to an example embodiment.
[0017] FIG. 15 illustrates an example placement of the microphone
array according to an example embodiment.
[0018] FIG. 16 illustrates an AI system with an ambient capture
device according to an example embodiment.
[0019] FIG. 17 is a flowchart illustrating a computer implemented
method of reducing the number of audio streams sent over a network
to the meeting server for use in generating a transcript according
to an example embodiment.
[0020] FIG. 18 is a flowchart illustrating a computer implemented
method for using both video and audio channels, audiovisual data,
from distributed devices to provide better speaker identification
according to an example embodiment.
[0021] FIG. 19 is a flowchart illustrating a computer-implemented
method for customizing output based on a user preference according
to an example embodiment.
[0022] FIG. 20 is a block schematic diagram of a computer system to
implement one or more example embodiments.
DETAILED DESCRIPTION
[0023] In the following description, reference is made to the
accompanying drawings that form a part hereof, and in which is
shown by way of illustration specific embodiments which may be
practiced. These embodiments are described in sufficient detail to
enable those skilled in the art to practice the invention, and it
is to be understood that other embodiments may be utilized, and
that structural, logical, and electrical changes may be made
without departing from the scope of the present invention. The
following description of example embodiments is, therefore, not to
be taken in a limited sense, and the scope of the present invention
is defined by the appended claims.
[0024] The functions or algorithms described herein may be
implemented in software in one embodiment. The software may consist
of computer executable instructions stored on computer readable
media or computer readable storage device such as one or more
non-transitory memories or other type of hardware-based storage
devices, either local or networked. Further, such functions
correspond to modules, which may be software, hardware, firmware or
any combination thereof. Multiple functions may be performed in one
or more modules as desired, and the embodiments described are
merely examples. The software may be executed on a digital signal
processor, ASIC, microprocessor, or other type of processor
operating on a computer system, such as a personal computer, server
or other computer system, turning such computer system into a
specifically programmed machine.
[0025] The functionality can be configured to perform an operation
using, for instance, software, hardware, firmware, or the like. For
example, the phrase "configured to" can refer to a logic circuit
structure of a hardware element that is to implement the associated
functionality. The phrase "configured to" can also refer to a logic
circuit structure of a hardware element that is to implement the
coding design of associated functionality of firmware or software.
The term "module" refers to a structural element that can be
implemented using any suitable hardware (e.g., a processor, among
others), software (e.g., an application, among others), firmware,
or any combination of hardware, software, and firmware. The term,
"logic" encompasses any functionality for performing a task. For
instance, each operation illustrated in the flowcharts corresponds
to logic for performing that operation. An operation can be
performed using, software, hardware, firmware, or the like. The
terms, "component," "system," and the like may refer to
computer-related entities, hardware, and software in execution,
firmware, or combination thereof. A component may be a process
running on a processor, an object, an executable, a program, a
function, a subroutine, a computer, or a combination of software
and hardware. The term, "processor," may refer to a hardware
component, such as a processing unit of a computer system.
[0026] Furthermore, the claimed subject matter may be implemented
as a method, apparatus, or article of manufacture using standard
programming and engineering techniques to produce software,
firmware, hardware, or any combination thereof to control a
computing device to implement the disclosed subject matter. The
term, "article of manufacture," as used herein is intended to
encompass a computer program accessible from any computer-readable
storage device or media. Computer-readable storage media can
include, but are not limited to, magnetic storage devices, e.g.,
hard disk, floppy disk, magnetic strips, optical disk, compact disk
(CD), digital versatile disk (DVD), smart cards, flash memory
devices, among others. In contrast, computer-readable media, i.e.,
not storage media, may additionally include communication media
such as transmission media for wireless signals and the like.
[0027] Individuals, referred to as users, can start a conversation
or meeting at any time. If a meeting had been scheduled,
arrangements could have been made to record the conversation and
create a transcript of the conversation for later reference.
However, ad-hoc meetings don't generally involve such preparation.
Stopping the meeting, or otherwise devoting time to set up a method
to record the conversation and arrange for a transcript to be
created may be distracting or may not be thought of during the
meeting. In addition, the ad-hoc meetings often take place outside
conference rooms. In this case, recording devices specifically
designed for meetings are not available.
[0028] During the conversation, the audio of the conversation may
be captured by devices the users may be carrying, referred to as
distributed devices. The captured audio signals may be transmitted
to a meeting system over wireless channels to recognize that
multiple users are having a conversation, referred to as a meeting,
which may or may not have been planned. If the meeting was
unplanned, it may be referred to as an ad-hoc meeting.
[0029] In response to a meeting having been detected or otherwise
arranged, a meeting instance is generated on the meeting system to
recognize speech from users that may be speaking and to generate a
transcript of the meeting. Multiple signals of speech from multiple
distributed devices may be received as separate audio channels and
used to generate the transcript. Distributed devices may include
personal user devices as well as other devices including digital
assistants, cameras, and any type of device that is capable of
receiving audio and/or video that may be within range of the
conversation.
[0030] In some embodiments, a meeting can be created with a single
press of a button on a single device via the meeting app. Other
devices and users with devices can join the meeting either through
the press of a button presented on the user device via the meeting
app, or by being recruited While not in use (e.g. an existing
conferencing device that is present in the room). Meeting
participants may be inferred by voice fingerprint, owners of
participating devices, facial recognition, and by manually adding a
user via a meeting application on their device at any point. (e.g.
for remote participants).
[0031] There are many different ways that a meeting may be
established. The distributed devices, such as smart phones, may be
associated with respective users and may include a meeting
application used to stream audio and/or video received from a
microphone and/or camera on the device to the meeting server. The
audio received from nearby devices will have an audio signature
based on a combination of ambient noise and/or any sound generated
near the device. In response to two user devices providing a
similar audio signature via their respective audio streams (audio
channels), the meeting system recognizes that a meeting may be
occurring and creates the meeting instance to process the audio
received. Users may be prompted via their meeting applications to
join the meeting, or other information, such as location
information, prior interactions, calendar information, or recent
email interactions for example, may be used to confirm that both
users or yet a third user should be added to the meeting
instance.
[0032] In further embodiments, an audio watermark may be generated
by one or more of the user devices. The audio watermark may
comprise the audio signature or may be separately detected. The
audio watermark may be a sound pattern having a frequency above the
normal hearing range of a user, such as 20 Khz or higher, or may
just be a sound that is inconspicuous to users so as not to
interfere with the conversation. In further embodiments, the
watermark may be completely audible and recognizable. The watermark
may be selected to be sent by a user desiring to ensure that a
meeting instance is created during a conversation in some
embodiments. The watermark will be received by distributed devices
within range and automatically or optionally added to a meeting
instance. Devices within range of the watermark sound may also have
their audio streams added to the meeting instance as additional
audio channels.
[0033] A conference code may be generated and sent to other users
to add them to a planned or ad-hoc meeting. The conference code may
also be selected ahead of a scheduled meeting and used in a meeting
invitation. The meeting system, on receiving the conference code
from a user device will add the audio stream from such user device
to the meeting once instantiated. In further embodiments, multiple
clusters of distributed devices may be joined using the conference
code. The clusters may be in different locations, such as different
halls, rooms, cities, countries, etc.
[0034] In one embodiment, output may be customized based on a user
preference in a distributed system are provided. In example
embodiments, the meeting server or system receives audio streams
from a plurality of distributed devices involved in an intelligent
meeting. An intelligent meeting is simply a meeting where one or
more devices are used to capture speech and create a transcript,
which may have speaker attribution. The meeting system identifies a
user corresponding to a distributed device of the plurality of
distributed devices and determines a preferred language of the
user. A transcript from the received audio streams is generated as
the meeting occurs. The meeting system translates the transcript
into the preferred language of the user to form a translated
transcript. The translated transcript is provided to the
distributed device of the user. In example embodiments, the
translated transcript is provided in real-time (or near real-time)
as the meeting is occurring. The translated transcript can be
provided via text (e.g., displayed on a device of the user) or
outputted as audio (e.g., via a speaker, hearing aid, earpiece). In
some embodiments, instead of or in addition to translation, other
types of transformation may be applied to the original transcript,
translated transcript, or translated speech audio.
[0035] FIG. 1 is a perspective view of a meeting 100 between
multiple users. A first user 110 has a first device 115 that
includes a microphone to capture audio, including speech. A second
user 120 has a second device 125 that is also capable of capturing
audio, including speech. The users may be seated at a table 130 in
one example meeting 100.
[0036] The first and second devices 115 and 125 transmit the
captured audio to a meeting server 135 for processing and
generation of a transcript. The meeting may be ad-hoc, in that it
was unplanned. The users may have run into each other on a break or
happened to meet each other in a hallway and decided to talk about
a project they are working on. A meeting application, or meeting
app may be running on both the first and second devices. The
meeting app may be used to provide the audio to the meeting server
135.
[0037] The meeting server 135 may detect that both devices are
sending audio with a similar audio signature, an audio watermark, a
meeting code provided by both devices, or other information
indicative of an ongoing discussion between the users. The meeting
server 135 may generate a meeting instance to process the received
audio and generate a transcript.
[0038] In various embodiments, a watermark may be any type of sound
having energies only above the human auditory range, which is about
20 kHz, or is otherwise inaudible, inconspicuous, or
non-distracting that identifies a meeting instance or meeting code
corresponding meeting 100. The watermark may be a sound encoding
the meeting code or other identification of the meeting instance in
further embodiments.
[0039] Meeting 100 may involve more than two people, whether
planned or ad-hoc. A third user 140 with a third device 145 may
also join in the meeting 100. The third device 145 may also provide
audio to the distributed meeting server 135. The audio may be
recognized as involved in the meeting 100 by one or more of the
same mechanisms described for recognizing that the first two
users/devices were involved in the meeting 100.
[0040] An owner/user of a distributed device may enroll
himself/herself via the app to be recognized by the meeting server
135. The user may already have a voice profile, referred to as a
voice thumbprint or fingerprint, to help the meeting server 135
associate an incoming speech sound with the user. If a random
person joins the meeting 100, the meeting server may recognize that
the person is not known, and prompt one or more of the users
already in a meeting for the person's name, or may also search a
database in an organization associated with known users in the
meeting to match the person with a profile. If the person is not
known or otherwise identified, the person may be identified with a
label or tag as speaker 1, speaker 2, etc. in a generated
transcript, making it easier to modify the transcript if the person
is later named. Any of the users may assign a name to the speaker
labels at any time during or after the meeting. Known contacts of
those already in the meeting may be used to reduce the
pool/database used to initially check for the person to optimize
the process of identifying the person.
[0041] There may be additional devices that are within audio or
visual range of the meeting 100, such as a digital assistant 148 or
a dedicated meeting device 150, both of which are shown on table
130, but could be anywhere within audio range of the meeting 100.
Such additional devices may also be connected to the distributed
meeting server 135 and have their audio streams added to the
meeting instance for processing to further enhance the audio and
speech to text processing capabilities of the meeting instance
running on meeting server 135. Such additional devices may be
detected by the server and added to the meeting as described above
or may be presented to one or more of the users as an option to add
to the meeting.
[0042] A video camera 155 or other camera may have a field of view
that encompasses the meeting 100. The meeting server 135 may be
aware of such cameras being near the meeting 100 and may provide an
indication to one or more of the users, providing an option to
obtain information from the cameras to the meeting instance to
further enhance the processing and provision of a transcript. For
instance, such cameras may be used to detect which user is
speaking, or at least provide information that a user is likely to
be speaking at any particular point in time.
[0043] FIG. 2 is a block diagram of a user device 200 for use in
meetings. Other devices that may participate in the meeting may
have a similar set of components. Device 200 includes at least one
microphone 210 and a processor 215 for executing a meeting app 220
that is stored on memory 225. A transceiver 230 is used for
streaming audio and/or video from a camera 235 to the distributed
meeting server 135. User device 200 may also have a display screen,
such as a touch screen 240, a portion of which is shown.
[0044] Devices that may be participating in the meeting can be
identified via calendar entries, current location, NFC (after
bringing phones very close together), Bluetooth.RTM. advertising,
and direct invitation via the conference code or other code that
may be generated and associated with the meeting 100.
[0045] The meeting server 135 may be processing several meetings at
the same time via multiple meeting instances. Each meeting instance
may include a meeting identifier, such as the meeting code,
identifications of devices that are streaming audio,
identifications of users that are participating in the meeting,
either via a user associated device, or otherwise recognized by the
meeting server by facial recognition, voice recognition, or other
means of recognizing users.
[0046] FIG. 3 is a flowchart illustrating a method 300 of
initiating an intelligent meeting between two users with associated
distributed devices. At operation 310 an audio watermark is
received at a first distributed device via a microphone associated
with the first distributed device. The audio watermark may be
transmitted by a speaker associated with a second distributed
device during a meeting. Data corresponding to the received audio
watermark is transmitted via the first distributed device to a
distributed device meeting server at operation 320. Note that the
received audio watermark may first be converted to digital form,
which may be simply a direct conversion of the audio watermark into
a digital representation of the sound or may include a decoding of
the audio watermark to obtain data identifying a meeting or the
second distributed device that emitted the audio watermark. An
indication may be received from the distributed meeting server at
operation 330 that the first distributed device has been accepted
to a meeting instance on the distributed device meeting server. The
first distributed device at operation 340 streams audio of the
meeting to the meeting instance on the distributed device meeting
server in response to the received indication. The received
indication may include information identifying a communication
channel to use, or the audio stream may simply identify the
streaming device which the meeting server uses to direct the audio
stream to the correct meeting instance.
[0047] FIG. 4 is a flowchart illustrating a method 400 of adding
distributed devices to an intelligent meeting using a conference
code. In some embodiments, the conference code may be encoded in a
watermark as in method 300. At operation 410, a conference code is
generated for a meeting between users via a first distributed user
device. The first distributed user device may receive the code from
a meeting server executing a meeting instance or may generate the
meeting code via a meeting app running on the first distributed
user device. The code may be sent to a second distributed user
device at operation 420. The code may be sent via email, text, or
other means of sending data electronically, or may be encoded as an
audible signal (audio watermark) and transmitted acoustically to
the rest of the participating devices, such as via a speaker of one
of the user devices, such as the first distributed user device. The
second distributed user may provide the conference code to the
meeting server meeting instance where the meeting code is used at
operation 430 to identify at least one second distributed user
device. The second distributed user device streams audio at
operation 440 to the meeting server meeting instance from both the
first and second distributed user devices.
[0048] The meeting may be an ad-hoc meeting between multiple users
with multiple respective user devices and the conference code is
generated after the ad-hoc meeting has started. Note that there may
also be users without an associated user device that are
participating in the meeting. Other user devices and devices not
associated with a user may be identified based on the detected
location of devices. Data from such devices may have their data
streams added to the meeting instance by providing a list of other
nearby devices to user(s) and allow selection of such devices via a
user interface of the app to add to the meeting instance. Devices
that may be participating in the meeting can be identified via
calendar entries, current location, NFC (bring phones very close
together), Bluetooth advertising, and direct invitation.
[0049] In further embodiments, the meeting is a planned meeting
between multiple users with multiple respective user devices and
the conference code is generated before the planned meeting has
started. The conference code may be sent to each of the user
devices and used by the corresponding apps to identify the devices
to the meeting server meeting instance for adding data streams from
such devices during the meeting.
[0050] FIG. 5 is a computer implemented method 500 of adding
further devices to an intelligent meeting. At operation 510 a
meeting server receives audio streams from a group of distributed
devices, wherein the audio streams comprise speech captured or
detected by such group of distributed devices during a meeting of
two or more users. The meeting server receives meeting information
at operation 520, corresponding to the meeting, from an additional
or new distributed device. The new device may be a user device
where the user has just joined the meeting, or the new device may
be a device that is in a room or otherwise within range of the
intelligent meeting. At operation 530, the additional distributed
device is added to the meeting server meeting instance. A stream of
information from the additional distributed device is received at
operation 540 in response to adding the additional distributed
device.
[0051] FIG. 6 is a flowchart illustrating a computer implemented
method 600 of detecting that an ad-hoc meeting is occurring. At
operation 610, audio streams are received at a meeting server from
two distributed devices that are streaming audio detected during an
ad-hoc meeting between two users. The audio streams are compared at
operation 620 to determine that the audio streams are
representative of sound from the ad-hoc meeting. The meeting server
generates a meeting instance at operation 630 to process the audio
streams in response to determining that the audio streams are
representative of sound from the ad-hoc meeting. At operation 640,
the audio streams are processed to generate a transcript of the
ad-hoc meeting.
[0052] The audio streams may be compared using many different
algorithms for comparing two signals. In one embodiment, the
comparison is performed by calculating the normalized cross
correlation coefficients between two signals. If the results are
above a selected threshold, the audio streams are mostly likely
from the same (ad-hoc) meeting. The selected threshold may be a
number between 0 and 1 and may be selected empirically based on
tests conducted during a number of meeting scenarios in different
environments. The selection may be performed to obtain a desired
balance of false negatives and false positives. Other indications
that the streams are from the same meeting include the location of
the devices. Further indications include users that have had
multiple contacts in the past, are in the same organization, and
other indications that the users are likely to meet. Further
verification can be obtained by comparing the text generated from
the audio streams.
[0053] Once the streams are successfully compared, a meeting
id(identifier)/code may be generated and used to add more
participants. Other participants may be added in response to
further devices streaming audio that is successfully compared to
the audio streams already in the meeting. Once a device is added,
the device may generate a signal indicative of joining the meeting,
such as a ping.
[0054] Users may also be authenticated prior to audio streams from
their respective devices being added to the meeting instance.
Authentication may be based on user confirmation from app, calendar
information, organization chart, use of the meeting code, extent of
contact/relationship with users already in the meeting, and other
means of authentication.
[0055] In one embodiment, the meeting server 135 detects when a
device and/or an associated user has left the meeting and removes
the audio stream/channel from the meeting instance. When a
participant associated with a device leaves a meeting, the meeting
server 135 detects the absence of the audio signal associated with
other device in the meeting and removes the device from the
meeting. Alternatives include the user signaling leaving via the
meeting app, closing the meeting app, detecting that the location
of the device is no longer near the location of the meeting,
detecting the absence of the corresponding audio watermark in the
video stream from a device, detecting that the audio signature
received by a device no longer matches that of other device audio
streams, and performing image recognition on images from video
signals to detect that the user is leaving or has left a conference
room or area where the meeting is taking place. Similarly, the
meeting instance can be concluded in response to a single user
remaining or single user device remaining.
[0056] FIG. 7 is a flowchart illustrating a computer implemented
method 700 of removing audio channels from user devices and other
devices in response to users leaving a meeting. At operation 710,
multiple audio signals received on corresponding multiple audio
channels received from a group of distributed devices receiving
audio from a distributed device meeting are processed by a meeting
server instance. The meeting server instance is used at operation
720 to detect that a first user associated with a first device of
the group of distributed devices has left the distributed device
meeting. At operation 730, the audio channel of the first
distributed device is removed from the multiple audio channels
being processed by the meeting server instance.
[0057] FIG. 8 is a flowchart illustrating a computer implemented
method 800 of authenticating a device for adding an audio stream
from the device to audio channels being processed by a meeting
server instance. Method 800 begins at operation 810 by receiving
audio streams at a meeting server from multiple distributed devices
receiving speech from multiple users during a meeting. The received
audio streams are processed at operation 820 via a meeting instance
executing on the distributed device meeting server to generate a
transcript based on speech included in the audio streams. At
operation 830, information is received at the meeting server from a
first additional distributed device associated with a first
additional user, the information corresponding to the meeting
between users. The information may correspond to a request to add
the user's device, or may be an implied request by noting that an
audio stream from such device includes a watermark or audio
signature. At operation 840, the first additional distributed
device or associated user is authenticated or otherwise authorized
to join the meeting. The first additional distributed device has
its audio stream added to the meeting instance at operation
850.
[0058] A participant may be authorized to join a meeting based on a
voice fingerprint, meeting organizer acceptance, using a meeting
code and/or new code, detected location of the device, comparison
of the device ID and/or associated user ID to an authorized list,
organization member check, use of a closed meeting flag to require
acceptance by the organizer, or combinations of the above. Note
that method 800 may also be applied to the first two devices to
join the meeting and may also be applied to devices that are not
directly associated with a user, such as a meeting assistant type
of device in a conference room or video camera having a field of
view of a meeting.
[0059] Remote participants may be connected into a meeting via
Skype a or Teams, or telephone dial-in or any other teleconference
application. If a remote conferencing platform like Skype is used,
the meeting may be joined by following a link sent out ahead of
time. For dial-in, a unique phone number or access code such as the
meeting code may be shared. Once the remote audio channel is
connected to the server for the meeting, it is processed in a way
similar to audio streams from the meeting area. If the speaker ID
is known based on the sign-in process, the audio stream may be a
single speaker, which means that no speech separation is required
unless a speakerphone is used with multiple remote users. The audio
being played by the speakerphone and captured by nearby distributed
devices in the meeting should be cancelled from audio streams from
such nearby distributed devices.
[0060] FIG. 9 is a high-level block flow diagram of a system 900
for generating a transcript for a meeting of multiple users. The
users may have an associated device 910, 912, 914 that are equipped
with microphones to capture audio, including speech by the various
users at the meeting and provide the captured audio as audio
signals to a meeting server, which includes at least a meeting
transcriber 925, via audio channels 916, 918 and 920, respectively.
Different devices may have slightly different clock cycles and
different amounts of processing latency. In addition, each
device-to-server connection channel may have a distinct latency.
Thus, the signals from audio channels 916, 918 and 920 are not
necessarily synchronized.
[0061] Meeting transcriber 925 may include a synchronization
function in addition to a speech recognition function. The audio
signals from channels 916, 918 and 920 are first synchronized and
then recognized, resulting in texts associated with each of the
channels in one embodiment. The recognition outputs are then fused
or otherwise processed to generate a transcript 940. The transcript
940 may then be provided back to the users for viewing. In other
embodiments, the audio signals from channels 916, 918, and 920 are
fused before speech recognition. The audio signal obtained after
the fusion is recognized, resulting in a single text for use in the
transcript. In some embodiments, the transcript may be provided
with very little delay.
[0062] In various embodiments, the conversion of the audio signals
to text that is used in conjunction with speaker identification,
and generation of a transcript that is diarized to identify
speakers, are provided by meeting server 135. The functions
performed by the server include the synchronization, recognition,
fusion, and diarization functions. While such functions are shown
in order in FIG. 9, in different embodiments, the functions may be
performed in varying orders. Fusion may be performed prior to
recognition and may also be performed at various other points as
described below.
[0063] FIG. 10 is a detailed block flow diagram illustrating
meeting server processing of information generally at 1000,
including audio streams from distributed devices. Multiple audio
data streams 1005 are received from multiple distributed devices.
The streams include M independent sequences of data packets. Each
packet of the mth sequence contains a segment of a digitized audio
signal captured by the mth device. The received packets are
unpacked and the data from the packets are reformed to create a
multi-channel signal. The multi-channel signal may be represented
as: {[x.sub.0(t), . . . , x.sub.M-1(t)]; t=0, 1, . . . }.
[0064] Synchronizing Audio Channels:
[0065] The digitized signals of different channels in the
multi-channel signal are likely not synchronized, since many of the
distributed devices are subject to digital signal processing
differences, subject to on-device software latency differences, and
subject to signal transmission speed differences. All of these
differences can add up, making it difficult to consolidate the
information from the different devices to create an accurate
transcript. A stream synchronization module 1015 receives the
multi-channel signal and selects one of the channels in it as a
reference channel. Without loss of generality, the first channel
can be used as the reference channel. For the reference channel,
the output is the same as the input, i.e., y.sub.0(t)=x.sub.0(t).
For the mth channel (0<m<M), the amount of misalignment
between x.sub.m(t) and x.sub.0(t) is estimated and corrected to
generate y.sub.m(t).
[0066] The degree of misalignment can be estimated by calculating
the normalized cross correlation coefficients between two signals,
using a sliding window for the non-reference channel signal and
picking up the lag that provides the maximum coefficient value.
This can be implemented by using a buffer to temporarily store
acoustic signal segments over which the cross-correlation analysis
is performed individually between the reference channel and each of
the other channels. Instead of the normalized cross correlation,
any score function that measures the degree of alignment between
the two signals can be used.
[0067] In one embodiment, the relationship between adjacent
synchronization cycles is taken into account. The misalignment is
caused by two factors: a device/channel-dependent offset and a
device-dependent clock drift. Even when two devices are capturing
an acoustic event at the same time, the signals captured by the
individual devices may arrive at the server at different times, due
to DSP differences, on-device software latency differences, signal
transmission speed differences, and so on. This is the
device/channel-dependent offset. Also, different devices inevitably
have slightly different clocks due to manufacturing variability.
Therefore, even if two devices claim to support, for example, a 16
kHz sampling rate, the signals recorded by these devices are not
100% aligned and the amount of mismatch linearly grows as time goes
on. This is the device dependent clock drift. The
device/channel-dependent offset and the device-dependent clock
drift are denoted as S and D. The time difference at the kth
synchronization cycle is represented as S+kD. Thus, estimates of S
and D, would provide a robust estimate of the degree of
misalignment, S+kD.
[0068] The amount of misalignment may be corrected by periodically
detecting misalignment using the above described cross-correlation
and correcting for such detected misalignment. In addition, to
reduce the amount of measured misalignment, a global offset
(device/channel-dependent) and device dependent clock drift may be
calculated to estimate the degree of misalignment. The global
offset may be used to correct global misalignment prior to
measuring and correcting the misalignment by cross-correlation. The
global offset may be determined as an average of measured
misalignment over time and is likely the result of clock drift in
the device. The degree of misalignment is thus estimated and
corrected by simply accounting for the difference from the
reference channel, Stream synchronization may be performed at
varying intervals, such as every 30 seconds. Other intervals less
than or greater than 30 seconds may be used in further embodiments,
as network latencies may change.
[0069] Stream synchronization module 1015 provides a multi-channel
synchronized signal, {[y.sub.0(t), . . . , y.sub.M-1(t)]; t=0, 1, .
. . } to a beamforming module 1020. The beamforming module 1020
functions to separate overlapping speech. Overlapping speech occurs
when two people in the meeting speak at the same time. Prior to
recognizing the speech and converting the speech to text, the
speech is first separated into separate channels. Thus with an
M-channel input, the output is N-channels, and is referred to as an
N-channel beamformed {[z.sub.0(t), . . . , Z.sub.N-1(t)]; t=0, 1, .
. . }. The stream synchronization module 1015 acts as a first
fusion point, where multiple outputs are generated to retain the
diversity of the input information. Where no speech overlaps, such
fusion is optional.
[0070] FIG. 11 is a flowchart illustrating a computer implemented
method 1100 of synchronizing multiple audio channels received from
multiple distributed devices during an intelligent meeting. At
operation 1110, audio signals representative of streamed speech are
received from multiple distributed devices to generate multiple
audio channels. A selected one of the audio channels is designated
at operation 1120 as a reference channel. Once the reference
channel is designated, the following operation are performed for
each of the remaining audio channels. At operation 1130, a
difference in time from the reference channel is determined. Each
remaining audio channel's time is aligned or compensated for by
correcting the remaining audio channels with the reference channel
as a function of the corresponding different in time at operation
1140. This can be done by simply dropping extraneous samples,
shifting signals, appending zeros, or using resampling
techniques,
[0071] Method 1100 may be performed periodically to correct the
timing of the remaining audio channels, such as every 30 seconds.
In one embodiment, method 1100 includes further operations to
correct for the global offset caused at least by different clocks
in the distributed devices. At operation 1150, a global offset is
determined for each of the remaining audio channels. The remaining
audio channels are then corrected at operation 1160 by each
corresponding remaining audio channel global offset prior to
correcting each remaining audio channel for the determined
difference in time.
[0072] Geometry-Agnostic, Beamforming:
[0073] Acoustic beamforming, or simply beamforming, is a technique
to enhance target speech by reducing unwanted sounds such as
background noise from multi-channel audio signals. It can improve
the accuracy of downstream speech processing, such as speech
recognition and speaker diarization.
[0074] For an intelligent meeting with audio streamed from multiple
distributed devices whose exact positions relative to one another
are not known, traditional beamforming algorithms, such as
delay-and-sum beamforming, superdirective beamforming, and
differential beamforming do not work. Such algorithms rely on prior
knowledge about the arrangement of microphone devices, which is not
available for distributed devices.
[0075] In one embodiment, an approach called geometry-agnostic
beamforming, or blind beamforming, is used to perform beamforming
for distributed recording devices. Given M microphone devices,
corresponding to NI audio channels, an M-dimensional spatial
covariance matrices of speech and background noise are directly
estimated. The matrices capture spatial statistics of the speech
and the noise, respectively. To form an acoustic beam, the
M-dimensional spatial covariance matrices are inverted.
[0076] A drawback of the beamforming approach, be it traditional
geometry-based beamforming or blind beamforming, is that it
typically reduces the number of information streams from M to one,
which means the downstream modules cannot take advantage of the
acoustic diversity provided by the spatially distributed devices.
In order to generate M beamformed signals and retain the acoustic
diversity, a leave-one-out approach can be taken. With this
approach, the first output signal is generated by performing
beamforming with Microphones 2-M. The second output signal is
generated with Microphone 1 and 3-M. This can be repeated M times
so that M different output signals are obtained. For each
beamforming, (M-1)-dimensional spatial covariance matrices are
computed and inverted, which is very computational demanding.
Fortunately, the computational cost can be significantly reduced by
deriving all the (M-1)-dimensional inverse matrices from the
original M-dimensional inverse matrices.
[0077] Separating Overlapped Speech:
[0078] In some embodiments, the beamforming module 1020 may be
configured to separate overlapped speech signals of different
users. This could make speech recognition and speaker attribution
more accurate. In one embodiment, continuous speech separation for
distributed microphone recording system is performed via a neural
network that is trained using permutation invariant training or its
variant such as deep clustering or attractor network. To
potentially save the computation, overlap detection may be used to
determine whether or not the speech separation neural network
should be executed for each period of time. If overlapped speech is
not detected for a selected period of time, the neural network is
not executed, saving processing resources and allowing the
transcript to be produced more quickly in real time.
[0079] The speech separation neural network model is executed to
perform continuous speech separation for distributed microphone
recording system, where the number of input microphones can be
arbitrary and vary through time. The neural network model is
configured to receive a varying number of inputs to support a
dynamic change in a number of audio signals and locations of
distributed devices during a meeting between multiple users. The
model outputs two continuous streams of speech. When there is one
active speaker, one of the outputting streams will be silent, while
when there is overlapping speech between two speakers, each speaker
will occupy a distinct output stream.
[0080] The speech separation neural network model contains three
submodules: the local observer, global summarizer and the mask
reconstructor. The multi-channel input is processed by those three
modules sequentially. Firstly, the same local observer is applied
to each input microphone. The local observer consists of a set of
stacked attention layer that maps each microphone input into a high
dimension representation, where each channel will cross compare and
extract the information from all other channels. Two different
types of attention were implemented, which are the self-attention
and feedforward attention.
[0081] Next, a summarizer is applied to summarize information from
each observer, to form a global representation across different
input channel. Two options for the summarizer are explored, which
are the mean pooling and a permutation invariant sorting algorithm,
where the representation of each channel is compared with
permutation invariant loss to align their local permutation and the
global permutation. When there is no summarization layer, the
network reduced with channel wise speech separation network, where
each channel has its own separation, i.e. no global separation
agreement between channels.
[0082] Lastly, a mask reconstructor sorts two mask outputs at the
same time for any arbitrary time. The reconstructor consists of a
stack of long short-term memory networks and generates the final 2
channel output from the summarization at each time point.
[0083] After getting two channel output from the mask
reconstructor, permutation invariant training objective function
was applied between the reconstructed mask and the clean reference,
where the Euclidean distance of each permutation pair of output and
clean reference are measured first, and then minimum distance and
corresponding permutation is selected to update the neural
network.
[0084] The network is trained with simulated multi-channel data,
where the number of input channel was randomly picked for each
sample, from 2 to 10 channels. Libri speech dataset was applied as
source data in the simulation. In each simulated sentence, two
utterances from two random speaker firstly selected, then each
utterance was processed with room acoustic simulation with room
impulse responses from image method with random room and location
setting. The overlap speech was generated by adding two simulated
speech, with an expected overlap ratio of 50%. 1000 hours of
training data was simulated. An Adam algorithm (a replacement
optimization algorithm for stochastic gradient descent for training
deep learning models) was applied for updating the neural
network.
[0085] One variation of the speech separation is the speech overlap
detection, where the task is reduced to just detect the overlap
region in recorded speech. The algorithm operates in a similar way,
where the network receives N channels as input, and continuously
outputs 2 channels as output. In the overlap detector, the network
doesn't output masks, instead, it outputs two 1 dimension indicator
functions, where 1 means there is one active speaker in that
channel, and 0 means silent. Therefore, when there are two active
speakers, the 2-output stream will all have 1 as output. When there
is one active speaker, an arbitrary channel will have 1 as output
and the other will have 0. The network is also trained with a
permutation invariant training objective between the output of the
network, i.e. indicator function, and the reference indicator.
[0086] FIG. 12 is a flowchart illustrating a computer implemented
method of separating overlapped speech in a distributed device
intelligent meeting. At operation 1210, audio signals
representative of speech is received via multiple audio channels
corresponding to streaming audio transmitted from corresponding
multiple distributed devices. Continuous speech separation is
performed at operation 1220 on the received audio signals to
separate speech from different speakers speaking at the same time
into separate audio channels. At operation 1230, the separated
audio channels are provided for speech recognition and generation
of a transcript. Operation 1230 in one embodiment provides a fixed
number of separate output channels. Since there may be a varying
number of microphone inputs, and the number of outputs is fixed in
advance, there may instances where a limited number of audio
channels can be accommodated, since for each audio channel with
multiple overlapping speakers, each speaker results in a separate
audio channel. Thus, if the number of output audio channels is
limited, not all channels may have speakers separated.
[0087] In one embodiment, speech separation at operation 1220 is
performed by a trained neural network model. The neural network
model is trained using permutation invariant training or its
variant.
[0088] The N different outputs of the beamforming module 1020 in
FIG. 10 are provided to N acoustic models 1025 and 1030 that
produce a sequence of senone posterior probabilities. Such models
are well known and are typically neural network based. The use of
an acoustic model for each of multiple audio channels from
distributed devices and/or beamformer outputs provides N scores for
each senone.
[0089] The scores, including those for the senones, are provided to
an acoustic model score fusion module 1035. The audio of the
individual input channels may be processed conventionally to
provide a sequence of senones and their posterior probabilities.
The results are combined using the model score fusion module 1035,
before applying the result to multiple speech recognition (SR)
decoders 1040, 1045. Score fusion module 1035 operates as a second
fusion point that combines multiple information sources and at the
same time generates multiple outputs to retain the diversity of the
input information. The two-step process involves two different
neural nets (or classifiers): a vanilla-flavor acoustic model and a
new more targeted acoustic model. The output is a sequence of lx
the number of senones. Note that the score fusion module 1035 uses
as input the output of the last layer of the acoustic model (neural
net) as input. In further embodiments, the score fusion module 1035
can use the output of any layer before the last. The size of the
input may be different than the size of the output.
[0090] The sequences of senones from the acoustic model score
fusion module 1035 are provided to the SR decoders 1040 and 1045,
each of which utilizes standard speech recognition processing to
provide an n-best list of words for each segment of senones. A
beginning time and a duration are provided for each word.
Segmentation may be performed based on voice activity detection,
speaker change detection, a fixed interval, or some other suitable
method. Rescoring may be performed by using a neural network
language model (NNLM) on the decoder output to generate better
n-best lists of word hypotheses.
[0091] Multiple speaker diarization modules 1050, 1055 receive the
outputs of the SR decoder modules as an N-best list for each
segment. In one implementation, only the top word sequence
hypothesis is used. A first operation extracts speaker embeddings,
such as d-vectors (hidden layer activations of a deep neural
network for speaker verification), at fixed intervals. A second
operation factorizes the word sequence into speaker-homogeneous
subsegments. This can be performed with variants of agglomerative
clustering, BIC (Bayesian Information Criterion), or other methods
by using the embedding features. A third operation assigns a
speaker ID to each of the subsegments obtained above by comparing
the proximity (e.g., cosine similarity, negative Euclidian
distance) of the speaker embeddings of the subsegment and those of
each candidate speaker. The resulting output is an assignment of a
speaker label to each recognized word of the top SR hypothesis.
[0092] A hypothesis combination module 1060 receives as input,
n-best lists from N SR decoder modules 1040, 1045 (e.g., beamformed
audio channels), and speaker recognition output from N sources such
as the beamformed/separated audio channels. Hypothesis combination
module 1060 processes the n-best scores from each channel by
scaling and normalizing them and thus computing utterance-level
posterior probabilities. The n-best hypotheses are aligned into
word confusion networks. By adding the utterance-level posteriors
pertaining to a given word hypothesis, word-level posterior
probabilities are obtained. The speaker recognition outputs from
each channel are formatted as confusion networks with alternating
speaker and word labels. The word labels are from the 1-best
recognition hypotheses, whereas the speaker labels represent 1-best
or n-best speaker model matching to the speech segments. Posterior
probabilities for the speaker hypotheses represent normalized
speaker model likelihoods. Posteriors on word hypotheses are scaled
down by two orders of magnitude to not affect the final word
recognition, thus affecting only the proper alignment of word and
speaker labels. The confusion networks thus obtained from each
channel are truncated and/or concatenated as necessary, to cover
the same time window, as dictated by online processing constraints.
The output comprises a confusion network (CN) encoding both word
and speaker hypotheses and their posterior probabilities.
[0093] The word and speaker confusion networks are aligned
according to a minimum edit distance criterion, as well as a
penalty for time discrepancies between aligned nodes. This
effectively merges the speaker and word hypotheses into a single
network, summing the posteriors of matching labels. If desired, the
top speaker and word hypotheses are read off from the combined CN
by picking the highest-posterior label at each position. The word
confusion networks may be built from word lattices instead of
n-best lists, depending on what the speech decoder outputs.
[0094] The output from combination module 1060 is the result of a
third fusion, referred to as a late fusion, to produce text and
speaker identification for generation of a speaker-attributed
transcript of the meeting. Note that the first two fusion steps at
beamforming module 1020 and acoustic model score fusion module
1035, respectively, are optional in various embodiments. In some
embodiments, one or more audio channels may be provided directly to
an acoustic model scoring module 1065 without beamforming or speech
separation. Speech recognition is then performed on such one or
more audio channels via SR decoder 1070, followed by speaker
diarization module 1075, with the output provided directly to
combination module 1060.
[0095] Audio streams may be fused early, following synchronization
of digital audio streams, by geometry-agnostic beam forming or
continuous speech separation. Multiple outputs may be generated to
retain input information diversity. Late fusing may be done at an
acoustic model score level and/or text level/diarization level to
leverage speaker information and diverse model hypotheses. In one
embodiment, late fusion over a word or two is performed by use of a
fixed time window. The time window in one embodiment corresponds to
salient audio events, and may be fixed at, for example, 2 seconds.
Such a time window is selected to be fairly short to enable the
provision of real-time transcripts with low latency.
[0096] Late fusion of data is performed by speech recognition for
multiple audio channels being processed in parallel, to produce
phrases. The phrases derived from the multiple audio channels are
combined in real time. In one embodiment, approximately two seconds
of speech is combined at hypothesis combination module 1060. Thus,
the audio streams are processed as they are received. A
non-overlapping sliding window of two seconds is used to process
the audio streams, decreasing the latency of the meeting server 135
transcript generation to close to zero.
[0097] The individual speech recognition decoders continuously
output some results and based on the hypothesis combination module
1060, the results are processed immediately. A special provision is
provided for the alignment of the individual systems at stream
synchronization module 1015, otherwise the final results may
contain multiple instances of the same events (due to
misalignment). A post-processing step removes any duplicates that
may exist regardless of the signal and/or speech recognition output
alignment. Alignment may be performed on either the word level or
on the sample level of the signals. Note also that different
versions of audio are received by the speech recognition decoders.
Each SR decoder may have heard something different. By combining
the SR results (late fusion) with low latency, a highly accurate
transcript is produced. Every SR outputs a word or two with a
confidence level. The time, such as two seconds, is long enough to
obtain some salient output, in other words, an output having a word
or two that can be recognized with some confidence. A fixed window
of time, such as two seconds was found to work better. If the time
is too short, there is no salient event, and if the time is too
long, latency becomes too long, and the transcript is delayed,
making the transcript of less utility during a meeting.
[0098] Another version of this approach is to wait for time points
in the audio stream where either (1) all streams contain no speech
with high confidence or (2) have a single word hypothesis with high
confidence. In those places the hypothesis space can be pinched to
a single hypothesis, which makes it possible to perform combination
without loss of accuracy as a result of incorrect word
segmentation.
[0099] The transcript may be provided to one or more of the meeting
participants as based on the output indicated at 1080. A single
meeting transcript is provided based on the output of the meeting
system. The transcript is composed of individual utterances and
associated media, such as slides or photos of drawings. Each
utterance is assigned a universal timestamp, attributed speaker,
associated text, and an associated audio segment, where the audio
is extracted from the synchronized input streams from all
participating clients.
[0100] Additional media such as images, notes, and other abstract
objects can be associated to the transcript inline through a
timestamp (e.g. a picture of a whiteboard was captured and uploaded
at time t) or to the whole meeting without a specific timestamp
(e.g. a file was uploaded after the meeting and associated with
this meeting instance). All attendees can have access to the
meeting and associated data. Ad-hoc meetings can be viewed and
modified by meeting owner, all attendees, or anyone depending on
the permissions set by the entity that created the meeting.
Additional services such as meeting summarization, action item
identification, and topic modeling may be provided using the
transcript and other associated meeting data.
[0101] Multilevel Fusion
[0102] FIG. 13 is a flowchart illustrating a computer implemented
method 13 of fusing audio streams at multiple selected points
during processing. The audio streams are recorded during a meeting
by a plurality of distributed devices. Method 13 is performed by
one or more processors performing operations. An operation 1310
performs speech recognition on each audio stream by a corresponding
speech recognition system executing on the one or more processors
to generate utterance-level posterior probabilities as hypotheses
for each audio stream. The hypotheses are aligned and formatted at
operation 1320 as word confusion networks with associated
word-level posterior probabilities. Operation 1330 performs speaker
recognition on each audio stream by execution of a speaker
identification algorithm that generates a stream of
speaker-attributed word hypotheses. The speaker hypotheses are
formatted with associated posterior speaker label posterior
probabilities and speaker-attributed hypotheses for each audio
stream as a confusion network at operation 1340. Operation 1350
aligns the word and speaker confusion networks from all audio
streams to each other to merge the posterior probabilities and
align word and speaker labels. A best speaker-attributed word
transcript is created at operation 1360 by reading off or selecting
the sequence of word and speaker labels with the highest posterior
probabilities.
[0103] In one embodiment, a special approximate version is obtained
when only a single word hypothesis from each stream is generated,
possibly even without posterior probabilities, and where simple
voting among all streams is used.
[0104] Method 1300 operations may be performed on successive time
windows applied to the audio streams such that the processing is
performed incrementally so as to enable production of
speaker-attributed word recognition hypotheses in real-time. The
input hypotheses are truncated in time to a common time window
applied to all audio streams based on the time marks associated
with the word hypotheses generated for each audio stream.
[0105] The input speaker and/or word hypotheses streams may
originate from multiple partial combination of input audio streams
via fusion of K out of N, where K<N, audio streams.
Alternatively, the input speaker and/or word hypotheses streams
originate not from different audio streams, but from multiple
partial combination of acoustic models applied to K out of N audio
streams, which in turn could result from raw audio signals or
fusion of audio signals.
[0106] In yet a further embodiment, the input hypotheses are
truncated in time to a common time window applied to all audio
streams based on the time marks associated with the word hypotheses
generated for each audio stream. The combination of K out of N raw
audio signals or fusion of the audio signals may be based on
audio-quality criteria and/or based on the relative position of the
speakers with respect to the distributed devices.
[0107] In one embodiment, the input speaker and/or word hypotheses
streams originate from multiple partial combination of input audio
streams via fusion of K out of N audio streams, where K<N. The
combination of K out of N acoustic model outputs may be based on
audio-quality criteria of the input signals and/or based on the
relative position of the speakers with respect to the distributed
devices. Alternatively, the input speaker and/or word hypotheses
streams may originate from multiple partial combinations of
acoustic models applied to K out of N audio streams where K<N,
which in turn result from raw audio streams or fusion of audio
streams. In yet a further embodiment, the output of multiple
acoustic models may be applied to K out of N audio streams, where
K<N, which in turn result from raw audio streams or fusion of
audio streams that are combined as input to M speech recognition
decoders.
[0108] Reducing Computational Resources in an Ambient Capture
Device:
[0109] FIGS. 14A and 14B illustrate an example ambient capture
device 1410. In an embodiment, ambient capture device 1410 may be
cylindrical in shape with a fisheye camera 1411 at the top of and
facing up with respect to the device. A microphone array 1413 may
be coupled to the device 1410 below the camera 1411 and placed
around the cylinder to capture audio in 360.degree.. It should be
noted that the device in FIG. 14A may not be drawn to scale. To
capture optimal 360.degree. vision (e.g., video or still images),
it may be desirable for the fisheye camera to be close to a floor
or table surface 1450. In an embodiment, the device may be short
and squat to avoid blind spots below the camera 1411. In an
embodiment, the fisheye camera may be placed in close proximity to
a microphone array 1413. In the example illustrated in FIG. 14B,
seven microphones 1423A-G are included in the microphone array
1413. As shown, six microphones 1423A-F may be placed around the
device in a plane and more or less equidistant from the center of
the device, and a seventh microphone 1423G may be placed in the
center. It will be understood that the device may be made of audio
penetrable material, such as a light fabric, grille, or mesh, and
that the microphones 1423 are not blocked by the fisheye camera
1421 or other structural portions of the device 1420, so that the
sound is not obstructed.
[0110] In an embodiment, the fisheye camera may be approximately 30
cm from the base of the device 1420, and the microphone array 1413
may be affixed approximately 15 cm above the base 1430. When in
operation, the device 1420 may sit on, or be affixed to, the floor
or table 1450 in an environment. As the device 1420 is placed
closer to the floor, the 360.degree. horizontal field of view
(HFOV) may include more of the environment. The fisheye camera 1421
is typically affixed to the device 1420 facing up, so the ceiling
may be in the field of view. It will be understood that other
shapes, sizes or configurations of the device 1420 and placement of
the fisheye camera 1421 and microphone array 1423 may be
implemented, with some adaptation to provide both similar and
varying results.
[0111] In an embodiment, acoustic parameters for audio capture may
vary depending on the specifications of the microphones. An example
of acoustic specifications for an embodiment are shown below in
Table 1. In an embodiment, the acoustic parameters may apply to the
whole audio subsystem, e.g., captured pulse code modulation (PCM)
data, not just the microphones. The captured audio may produce
adequate speech recognition accuracy for use in an AI application.
One of ordinary skill in the art, with the benefit of the present
disclosure, will appreciate that various acoustic parameters may be
utilized to achieve speech recognition accuracy, and that the
example parameters in Table 1 are for illustrative purposes.
TABLE-US-00001 TABLE 1 Example Acoustic Parameters Sensitivity (1
kHz -26 +/- .ltoreq.0.1 dB FS 94 dB SPL) Signal-noise ratio
.gtoreq.64 dB A (SNR), including power supply and digital filter
noise Frequency Response 50 -> 16 kHz (+/-.ltoreq.3 dB) Total
Harmonic .ltoreq.1% (105 dB SPL) Distortion .ltoreq.5% (115 dB SPL)
Directionality Omnidirectional (.ltoreq.1 dB sensitivity difference
for 50 -> 16 kHz) Variance between .ltoreq.1 dB sensitivity
difference for microphones 50 -> 16 kHz Longevity No permanent
loss of performance at: Maximum SPL .gtoreq.160 dB Maximum shock
.gtoreq.10,000 g Temperature Range -40.degree. C. to +80.degree.
C.
[0112] FIG. 15 illustrates an example placement of the microphone
array 1523, according to an embodiment. In an embodiment, the
device includes seven microphones placed in the same plane. Six
microphones 1523A-F may be placed in a circular or hexagonal
pattern in the plane, approximately 4.25 cm from a center point. A
seventh microphone 1523G may be placed at the center point. In an
embodiment, the configuration of seven microphones comprise
microphones of similar specification. It will be understood that
additional processing of the audio data received from the
microphone array may be necessary to normalize or adjust the audio
when the microphones are dissimilar. In an example implementation,
the microphone array 1523 may comprise seven digital
microelectromechanical systems (MEMS) microphones with ports facing
upwards. It will be understood that better performance may result
when the microphones are not obstructed by sound absorbing or
blocking components, such as a circuit board or device case. In an
embodiment, similar microphones are clocked using the same clock
source in the device (not shown). The clocking or timestamping of
the audio may assist with synchronization and fusion of the
audiovisual data. The ambient capture device may decimate all
microphone signals to 16-bit 16 kHz PCM data. In this context,
decimation is the process of reducing the sampling rate of the
signal. For automatic speech recognition, frequency bands higher
than 8 kHz may be unnecessary. Therefore, a sampling rate of 16 kHz
may be adequate. Decimation reduces bit rate without compromising
required accuracy. In an embodiment, the capture device may support
additional bit depths and sampling frequencies. In an embodiment,
the capture device may not allow changing data width and sampling
frequency, to reduce driver complexity and improve stability. The
microphones may be mounted using any adequate mechanical dampening
mechanism, for instance, rubber gaskets, to reduce vibrations and
noise. It will be understood that more or fewer microphones may be
present in the microphone array. However, fewer microphones may
introduce some uncertainty of speaker location or direction.
Additional microphones may provide increased certainty or
resolution of the audio, but at a cost of more hardware and
additional complexity of calculation.
[0113] In an embodiment, an audio speaker may be located at the
bottom, or base, of the device, for audio feedback to the user. The
audio speaker may be used for feedback announcements or be an
integral part of the AI application. For instance, in an AI
application for conference management, a user may request meeting
minutes to be read back to the attendees. An integrated speaker in
the device may provide feedback or request instructions or commands
for operation. If a spoken command is not understood, a request to
repeat the command may be played through the speaker. To reduce
acoustic feedback, the audio speaker may face the opposite
direction from the microphone array. Audio played back via the
audio speaker may be looped back as an additional synchronized
microphone channel.
[0114] Referring back to FIG. 14B, in an embodiment, the fisheye
camera 1421 may receive 360.degree. HFOV, and at least 95.degree.
vertical field of view (VFOV) above, and 95.degree. VFOV below a
horizontal axis, resulting in a 190.degree. VFOV, or approximately
200.degree. diagonal field of view (DFOV). In practice, the capture
device may be placed on a table of floor, so a vertical view below
the surface may not be needed. Thus, in discussion herein, the VFOV
is identified as approximately 95.degree. to indicate a view above
the horizontal base plane of the device. In an embodiment, the
fisheye camera 1421 may include one fisheye sensor of 12 megapixels
(MP) (e.g., providing a 4K resolution). The camera lens may be
mounted with respect to its image sensor, so that the optical
center aligns with the center of the image sensor, and the optical
axis is perpendicular to the image sensor. The relative position of
the camera module to the microphone array may be fixed and known.
In particular, the optical center may also align with the center of
the microphone array, with the optical axis perpendicular to the
microphone array.
[0115] FIG. 16 illustrates an AI system 1600 with an ambient
capture device 1610, as described above, and a meeting server,
referred to as a cloud server 1620. In an example, user 1630
interacts with an AI application 1623. It will be understood that
the AI application may reside on the cloud server 1620 or on a
local device (not shown). Audiovisual data may be captured in
360.degree. by the AI capture device 1610. As discussed above, the
capture device 1610 may include a fisheye camera 1611 providing a
360.degree. HMV and approximately a 95.degree. VFOV. The capture
device 1610 may include a microphone array 1613 to capture audio in
360.degree.. Video compression of the images and video stream
received by the camera 1611 may be performed by a processor 1615 on
the device. Video modes and compression protocols and criteria may
be controlled by user selectable software controls. In addition to
compression, the audiovisual data may be protected by encryption,
to prevent unauthorized persons from obtaining the data. In an
embodiment, compression 1618 may be performed by circuitry on the
device and controlled by software switches. Pre-processing 1617
(e.g., cropping of images based on image content, or noise
reduction) may be performed by logic executed by the processor,
before compression 1618. In an embodiment, pre-processing may
include acoustic echo cancellation (AEC) to reduce feedback, noise,
and echo caused by a speaker 1612 coupled to the device. In an
embodiment, a local process for keyword spotting (KWS) may be
included in order to listen for device commands for the ambient
capture device, such as to wake or tum off the device. The local
KWS may favor recall vs. precision, and it may be based on a
reduced microphone array (e.g., two microphones rather than the
full array). When AEC is performed on the device 1610, the acoustic
channel including the speaker audio may not need to be sent to the
models to perform sensor fusion 1621. The compressed audiovisual
data may be sent to a cloud server 1620 by a transmission unit
1619. Transmission unit 1619 may include one or more of: a network
interface card for wired communication, such as an Ethernet
connection; a wireless transceiver using a wireless protocol such
as for Wink, Bluetooth.RTM., NFC; or other communication means. In
an embodiment, audio feedback may be sent to the device via one of
the wireless channels. The cloud server 1620 may perform sensor
fusion 1621 for the AI application 1623. Therefore, compression may
be performed to reduce bandwidth of the transmission to the cloud
via a transmission unit 1619.
[0116] Capture device 1410 may be used with distributed devices in
capturing audio and video from a distributed device meeting. Device
1410 may itself be one of the distributed devices. The
identification of users associated with speech may be performed
solely by capture device 1410 in one embodiment, of the information
streams collected from capture device 1410 may be used together
with information streams collected from the other distributed
devices to generate speaker attributed transcripts in various
embodiments.
[0117] FIG. 17 is a flowchart illustrating a computer implemented
method 1700 of reducing the number of audio streams sent over a
network to the meeting server for use in generating a transcript.
Method 1700 begins by receiving multiple channels of audio at
operation 1710 from three or more microphones detecting speech from
a meeting of multiple users. At operation 1720, directions of
active speakers are estimated. A speech unmixing model is used to
select two channels that may correspond to a primary and a
secondary microphone at operation 1730, or may correspond to a
fused audio channel. The two selected channels are sent at
operation 1740 to a meeting server for generation of an intelligent
meeting transcript. By reducing the amount of data sent to the
meeting server, bandwidth is conserved. Since the data selected is
arguably the best data, little if any accuracy is lost.
[0118] In one embodiment, the microphones are supported by a device
in a fixed configuration. The fixed configuration may include a
camera having a field of view configured to include the multiple
users. Localizing sound sources may be performed by executing a
model trained on channels of audio and video from the camera. For
example, if one user is using a laptop computer with a camera, the
laptop may provide both an audio and video channel. The audio
channel may be synchronized with respect to the reference audio
channel, and the same time difference may be used to synchronize
the video channel. Image recognition may be used on the video
channel to identify the user as the speaker for diarization in
producing the transcript. In a further embodiment, the laptop
computer may perform image processing to determine that the user is
speaking and provide a tag on the audio channel identifying the
user as the speaker and indicating whether the user is speaking or
not speaking. The tag may then be used for diarization without the
need to transmit the video channel from the laptop.
[0119] In a further embodiment, the microphones are associated with
multiple distributed devices. The distributed devices may include
wireless devices respectively associated with the multiple users.
At least one of the distributed devices may include a camera
providing video of at least one of the users.
[0120] In yet a further embodiment, the microphones include
microphones supported in a fixed configuration and microphones
associated with distributed devices associated with users. The
method may be performed by one or more of a device supporting the
microphones in a fixed position or an edge device receiving the
multiple channels of audio. The speech unmixing model may be
executed on the edge device.
[0121] In further embodiments, client-side processing (processing
on one or more of the distributed devices, ambient capture device,
and/or edge server) is used to reduce the computational resources
required by the meeting server as well to reduce the amount of
network bandwidth utilized for processing distributed meeting
information streams from the distributed devices. In addition to
the reduction in the number of streams sent via network to the
meeting server as described above, beamforming may be performed on
the client side, as well as generation of audio watermarks and
meeting codes. In further embodiments, model sizes may be reduced
and quantized to better run on the client side. The objective
function may also be modified to better run on the client size.
Instead of outputting a speech mask, sound source localization may
be used with commensurate less computations.
[0122] Both audio and video channels may be used to attribute
speech to users for creation of the diarized transcript. An
audiovisual diarization approach allows the combining of voice
identification, sound source localization, face
tracking/identification, and visual active speaker detection from
distributed sensors to achieve robust diarization.
[0123] FIG. 18 is a flowchart illustrating a computer implemented
method 1800 for using both video and audio channels, audiovisual
data, from distributed devices to provide better speaker
identification. Method 1800 begins by receiving, at operation 1810,
information streams on a meeting server from a set of multiple
distributed devices included in an intelligent meeting. At
operation 1820, audio signals representative of speech by at least
two users in at least two of the information streams are received.
At operation 1830, at least one video signal of at least one user
in the information streams is received. The received audio and
video signals are used to associate speech in the received audio
signals with a specific user as a function of the received audio
and video signals at operation 1840. A transcript of the
intelligent meeting is generated at operation 1850 with an
indication of the user associated with the speech.
[0124] The multiple distributed devices in one embodiment are
mobile wireless devices associated with users in the intelligent
meeting. The mobile wireless devices may include a microphone, and
also may include a camera that provides the at least one video
signal. In further embodiments, the multiple distributed devices
include a device having multiple microphones supported in a fixed
configuration, each microphone providing one of the received audio
signals. The device may include a camera having a field of view
configured to include multiple users in the intelligent meeting and
provide the at least one video signal.
[0125] In one embodiment, a fusion model is used on the received
audio and video signals to associate the specific user with the
speech. In an embodiment, the audiovisual data may be analyzed by
the meeting server. The audiovisual data may be first be compressed
prior to sending to the meeting server via a network. In another
embodiment, the fusion model be coupled to the capture device as an
integrated system. Discussions herein describe the meeting server
for illustration purposes and not as a limitation.
[0126] The meeting server decompresses, decodes or decrypts the
data, as necessary. The audiovisual data may be fused and analyzed
by an AI application utilizing an LSTM model, for example, to
identify or infer features in the audiovisual data such as, but not
limited to: audio direction; speaker location in an image; speaker
movement; voice signature; facial signature; gesture; and/or
object. In an example, an AI application may require speech
recognition or facial recognition, The LSTM model(s) may be trained
with data specific to the AI application using the sensor data. In
an embodiment, more than one model or analysis engine may be used,
as discussed above.
[0127] In an embodiment, speech may be identified and gesture
recognition using the video data may be performed. The LSTM model
may use the identified speech and the recognized gesture to provide
a probable fusion of the data, and send the probable outcomes to
the AI application. In an example, a gesture combined with a voice
command may provide specific control commands to the AI
application. In an example, analysis of video data may indicate an
eve gaze or track eye movements to infer where a user is looking.
Eye gaze analysis may result in control commands for the AI
application, and may differ based on fusion with audio data. In an
embodiment, the LSTM model may be trained for a specific AI
application and provide the control or commands for that
application, based on the fused data. In another embodiment, the
LSTM model may be more generic, and provide probable correlated
data, such as audio streams for each speaker with a speaker ID and
location in the environment, and a video stream, to the AI
application for further processing and interpretation of the
inputs. In this example, the AI application may use the audio and
video stream input to derive the appropriate commands or perform
actions.
[0128] One embodiment utilizes a fisheye camera with a 12MP sensor.
Another embodiment may include an infrared (IR) or other depth
sensor to provide three dimensional (3D) or depth information.
Depth information may not be available in 360.degree. if there are
not enough depth sensors to cover the entire HFOV. Variations of
the capture device may be provided to accommodate various price
points acceptable to a wide range of users, or for different
applications. For instance, inclusion of the depth sensors or
higher resolution sensors may increase the cost or complexity of
the device beyond what is necessary for the selected AI
application.
[0129] FIG. 19 is a flowchart illustrating a computer-implemented
method 1900 for customizing output based on a user preference
according to an example embodiment. Operations in the method 1900
are performed by the meeting server or system e.g., meeting server
135), using components described above. Accordingly, the method
1900 is described by way of example with reference to the meeting
server. However, it shall be appreciated that at least some of the
operations of the method 1900 may be deployed on various other
hardware configurations or be performed by similar components
residing elsewhere in a network environment. Therefore, the method
1900 is not intended to be limited to the meeting server.
[0130] In operation 1910, the meeting server receives audio streams
from a plurality of distributed devices. In example embodiments,
the audio streams comprise speech captured by one or more of the
plurality of distributed devices during a meeting of two or more
users. In some embodiments, the meeting is an ad-hoc meeting. In
these embodiments, the server can perform blind beamforming or
continuous speech separation on the received audio streams to
separate speech from background noise or different speakers
speaking at the same time into separate audio channels. In some
cases, the audio streams are compared to determine that the audio
streams represent sound from the (same) ad-hoc meeting. A meeting
instance is then generated to process the audio streams identified
as being from the ad-hoc meeting.
[0131] In operation 1920, an identity of a user of one of the
distributed devices is identified by the meeting server. In one
embodiment, the user is identified based on a video signal captured
by a camera (e.g., camera 155, camera 1521) associated with the
meeting. The video signal is transmitted to the meeting server. The
meeting server compares an image of the user from the video signal
with stored images of known (e.g., registered) users to determine a
match. If a stored image matches a captured image of the user in
the video signal, then the user is identified. In one embodiment,
the image of the user is stored or associated with a user profile
of the user.
[0132] In an alternative embodiment, the user is identified based
on a voice signature. In this embodiment, speech from the audio
stream is parsed or diarized and compared to stored voice
signatures of known users. If a stored voice signature matches the
parsed/diarized speech from the audio stream, then the user is
identified. In one embodiment, the voice signature of the user is
stored or associated with a user profile of the user.
[0133] In operation 1930, a language preference of the identified
user is determined. In some embodiments, a user profile of the
identified user that is accessed. The user profile comprises at
least a predetermined preference for a language of the user. In
some cases, the predetermined preference is established (e.g.,
explicitly indicated) by the user. In other cases, the
predetermined preference is determined based on a device
configuration of the device (e.g., distributed device such as a
cellphone or laptop) associated with the user. For example, the
device may be configured to function in English or Chinese.
[0134] In operation 1940, the meeting server generates a transcript
as discussed above. In example embodiments, speech from the audio
streams are converted to text in order to generate a text-based
transcript or digital transcript. In one embodiment, as discussed
above, a real-time transcript is generated based on short word
sequences. Late fusion of data may be performed by speech
recognition for multiple audio channels being processed in parallel
to produce phrases. The phrases derived from the multiple audio
channels are combined in real-time or near real-time. In one
embodiment, approximately two seconds of speech is combined. As a
result, the audio streams are essentially processed as they are
received. A non-overlapping sliding window of a few seconds, such
as two seconds, is used to process the audio streams, decreasing
latency for transcript generation.
[0135] In operation 1950, the meeting server translates the
transcript according to the language preference of the user. In
some embodiments, the meeting server takes the generated transcript
from operation 1940 and translates the text in the generated
transcript into text in the preferred language. In other
embodiments, the meeting server takes the generated transcript from
operation 1940 and converts the generated transcript into speech in
the preferred language. Further still, some embodiments may perform
both text translation and speech translation.
[0136] In operation 1960, the translated transcript is provided to
a device (e.g., distributed device) of the user. In some
embodiments, the device comprises a same device that is used to
capture audio from the user. The translated transcript can
provided, for example, as a text displayed on a display device
(e.g., screen) of the device, or as a speech audio via speaker
devices (e.g., earpieces, hearing aids or loudspeakers) by using
text-to-speech. In some embodiments, the diarization results may
also be provided.
[0137] While the method 1900 of FIG. 19 is described having
operations in a particular order, alternative embodiments may
perform the method 1900 with operations in a different order. For
example, identifying the user (operation 1920) and determining the
language preference (operation 1930) can occur after or while the
transcript is generated (operation 1940) and prior to translating
the transcript (operation 1950).
[0138] FIG. 20 is a block schematic diagram of a computer system
2000 to implement and manage the handling of intelligent meetings
via multiple distributed devices, edge devices, and cloud based
devices and for performing methods and algorithms according to
example embodiments. All components need not be used in various
embodiments.
[0139] One example computing device in the form of a computer 2000
may include a processing unit 2002, memory 2003, removable storage
2010, and non-removable storage 2012. Although the example
computing device is illustrated and described as computer 2000, the
computing device may be in different forms in different
embodiments. For example, the distributed devices may be a
smartphone, a tablet, smartwatch, or other computing device
including the same or similar elements as illustrated and described
with regard to FIG. 20. Devices, such as smartphones, tablets, and
smartwatches, are generally collectively referred to as mobile
devices, distributed devices, or user equipment.
[0140] Although the various data storage elements are illustrated
as part of the computer 2000, the storage may also or alternatively
include cloud-based storage accessible via a network, such as the
Internet, server-based storage, or a smart storage device (SSD).
Note also that an SSD may include a processor on which the parser
may be run, allowing transfer of parsed, filtered data through I/O
channels between the SSD and main memory.
[0141] Memory 2003 may include volatile memory 2014 and
non-volatile memory 2008. Computer 2000 may include or have access
to a computing environment that includes--a variety of
computer-readable media, such as volatile memory 2014 and
non-volatile memory 2008, removable storage 2010 and non-removable
storage 2012. Computer storage includes random access memory (RAM),
read only memory (ROM), erasable programmable read-only memory
(EPROM) or electrically erasable programmable read-only memory
(EEPROM), flash memory or other memory technologies, compact disc
read-only memory (CD ROM), Digital Versatile Disks (DVD) or other
optical disk storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or any other medium
capable of storing computer-readable instructions.
[0142] Computer 2000 may include or have access to a computing
environment that includes input interface 2006, output interface
2004, and a communication interface 2016. Output interface 2004 may
include a display device, such as a touchscreen, that also may
serve as an input device. The input interface 2006 may include one
or more of a touchscreen, touchpad, mouse, keyboard, camera one or
more device-specific buttons, one or more sensors integrated within
or coupled via wired or wireless data connections to the computer
2000, and other input devices. The computer may operate in a
networked environment using a communication connection to connect
to one or more remote computers, such as database servers. The
remote computer may include a personal computer (PC), server,
router, network PC, a peer device or other common data flow network
switch, or the like. The communication connection may include a
Local Area Network (LAN), a Wide Area Network (WAN), cellular,
Wi-Fi, Bluetooth, or other networks. According to one embodiment,
the various components of computer 2000 are connected with a system
bus 2020.
[0143] Computer-readable instructions stored on a computer-readable
medium are executable by the processing unit 2002 of the computer
2000, such as a program 2018. The program 2018 in some embodiments
comprises software to implement one or more methods for
implementing the meeting app and meeting server, as well as the
modules, methods, and algorithms described herein. A hard drive,
CD-ROM, and. RAM are some examples of articles including a
non-transitory computer-readable medium such as a storage device.
The terms computer-readable medium and storage device do not
include carrier waves to the extent carrier waves are deemed too
transitory. Storage can also include networked storage, such as a
storage area network (SAN). Computer program 2018 along with the
workspace manager 2022 may be used to cause processing unit 2002 to
perform one or more methods or algorithms described herein.
EXAMPLES
[0144] 1. A computer implemented method includes receiving multiple
channels of audio from three or more microphones detecting speech
from a meeting of multiple users, localizing speech sources to
determine an approximate direction of arrival of speech from a
user, using a speech unmixing model to select two channels
corresponding to a primary and a secondary microphone, and sending
the two selected channels to a meeting server for generation of a
speaker attributed meeting transcript.
[0145] 2. The method of example I wherein the microphones are
supported by a device in a fixed configuration.
[0146] The method of example 2 wherein the fixed configuration
includes a camera having a field of view configured to include the
multiple users.
[0147] 4. The method of example 3 wherein localizing speech sources
comprises using a model trained on channels of audio and video from
a device having a same fixed configuration.
[0148] 5. The method of any of the previous examples wherein the
microphones are associated with multiple distributed devices.
[0149] 6. The method of example 5 wherein the distributed devices
comprise wireless devices respectively associated with the multiple
users.
[0150] 7. The method of example 6 wherein at least one of the
distributed devices includes a camera providing a video stream of
at least one of the users, wherein the distributed device with the
camera provides an audio stream synchronized with the video stream
such that synchronization of the audio stream with other audio
streams provides synchronization information for synchronizing the
video stream.
[0151] 8. The method of any of the previous examples wherein the
microphones include microphones supported in a fixed configuration
and microphones associated with distributed devices associated with
users.
[0152] 9. The method of any of the previous examples wherein the
method is performed by an ambient device supporting the microphones
in a fixed position or a client-side device receiving the multiple
channels of audio.
[0153] 10. The method of example 9 wherein the speech unmixing
model is executing on the client-side device.
[0154] 11. The method of example 9 wherein localizing speech
sources is performed on a client-side device.
[0155] 12. A machine-readable storage device has instructions for
execution by a processor of a machine to cause the processor to
perform operations to perform a method. The operations include
receiving multiple channels of audio from three or more microphones
detecting speech from a meeting of multiple users, localizing
speech sources to determine an approximate direction of arrival of
speech from a user, using a speech unmixing model to select two
channels corresponding to a primary and a secondary microphone, and
sending the two selected channels to a meeting server for
generation of a speaker attributed meeting transcript.
[0156] 13. The device of example 12 wherein the microphones are
supported by a device in a fixed configuration.
[0157] 14. The device of example 13 wherein the fixed configuration
includes a camera having a field of view configured to include the
multiple users.
[0158] 15. The device of example 14 wherein the fixed configuration
includes a camera having a field of view configured to include the
multiple users and wherein localizing speech sources comprises
using a model trained on channels of audio and video from a device
having a same fixed configuration.
[0159] 16. The device of any of the previous examples wherein the
microphones are associated with multiple distributed devices,
wherein the distributed devices comprise wireless devices
respectively associated with the multiple users, and wherein at
least one of the distributed devices includes a camera providing a
video stream of at least one of the users, wherein the distributed
device with the camera provides an audio stream synchronized with
the video stream such that synchronization of the audio stream with
other audio streams provides synchronization information for
synchronizing the video stream.
[0160] 17. The device of any of the previous examples wherein the
microphones include microphones supported in a fixed configuration
and microphones associated with distributed devices associated with
users.
[0161] 18. The device of any of the previous examples wherein
localizing speech sources is performed on a client-side device.
[0162] 19. A device includes a processor and a memory device
coupled to the processor and having a program stored thereon for
execution by the processor to perform operations. The operations
include receiving multiple channels of audio from three or more
microphones detecting speech from a meeting of multiple users,
localizing speech sources to determine an approximate direction of
arrival of speech from a user, using a speech unmixing model to
select two channels corresponding to a primary and a secondary
microphone, and sending the two selected channels to a meeting
server for generation of a speaker attributed meeting
transcript.
[0163] 20. The device of example 19 wherein the microphones are
supported by a device in a fixed configuration that includes a
camera having a field of view configured to include the multiple
users and a camera having a field of view configured to include the
multiple users and wherein localizing speech sources comprises
using a model trained on channels of audio and video from a device
having a same fixed configuration.
[0164] Although a few embodiments have been described in detail
above, other modifications are possible. For example, the logic
flows depicted in the figures do not require the particular order
shown, or sequential order, to achieve desirable results. Other
steps may be provided, or steps may be eliminated, from the
described flows, and other components may be added to, or removed
from, the described systems. Other embodiments may be within the
scope of the following claims.
* * * * *