U.S. patent application number 15/346676 was filed with the patent office on 2018-05-10 for splitting utterances for quick responses.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Tohru Nagano, Ryuki Tachibana.
Application Number | 20180130460 15/346676 |
Document ID | / |
Family ID | 62063962 |
Filed Date | 2018-05-10 |
United States Patent
Application |
20180130460 |
Kind Code |
A1 |
Nagano; Tohru ; et
al. |
May 10, 2018 |
SPLITTING UTTERANCES FOR QUICK RESPONSES
Abstract
Methods, a system, and a classifier are provided. A method
includes preparing, by a processor, pairs for an information
retrieval task. Each pair includes (i) a training-stage speech
recognition result for a respective sequence of training words and
(ii) an answer label corresponding to the training-stage speech
recognition result. The method further includes obtaining, by the
processor, a respective rank for the answer label included in each
pair to obtain a set of ranks. The method also includes
determining, by the processor, for each pair, an end of question
part in the training-stage speech recognition result based on the
set of ranks. The method additionally includes building, by the
processor, the classifier such that the classifier receives a
recognition-stage speech recognition result and returns a
corresponding end of question part for the recognition-stage speech
recognition result, based on the end of question part determined
for the pairs.
Inventors: |
Nagano; Tohru; (TOKYO,
JP) ; Tachibana; Ryuki; (KANAGAWA-KEN, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
62063962 |
Appl. No.: |
15/346676 |
Filed: |
November 8, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 7/005 20130101;
G06N 20/00 20190101; G06F 16/3329 20190101; G10L 15/063
20130101 |
International
Class: |
G10L 15/06 20060101
G10L015/06; G10L 15/16 20060101 G10L015/16; G10L 15/02 20060101
G10L015/02; G10L 15/197 20060101 G10L015/197; G10L 15/30 20060101
G10L015/30; G06F 17/30 20060101 G06F017/30; G06N 99/00 20060101
G06N099/00 |
Claims
1. A method for building a classifier, comprising: preparing, by a
processor, a plurality of pairs for an information retrieval task,
each of the plurality of pairs including (i) a training-stage
speech recognition result for a respective sequence of training
words and (ii) an answer label corresponding to the training-stage
speech recognition result; obtaining, by the processor using a
search engine, a respective rank for the answer label included in
each of the plurality of pairs to obtain a set of ranks;
determining, by the processor, for each of the plurality of pairs,
an end of question part in the training-stage speech recognition
result based on the set of ranks; building, by the processor, the
classifier such that the classifier receives a recognition-stage
speech recognition result and returns a corresponding end of
question part for the recognition-stage speech recognition result,
based on the end of question part determined for the plurality of
pairs; splitting, by the processor, the recognition-stage speech
recognition result at the corresponding end of question part for
the recognition-stage speech recognition result; generating, by the
processor, a set of answer candidates for replying to the
corresponding end of question part for the recognition-stage speech
recognition result; and providing, by a display device, the set of
answer candidates to a user.
2-3. (canceled)
4. The method of claim 1, wherein the end of question part for a
given one of the plurality of pairs is determined based on a rank
change of a part of the respective sequence of training words
included in the training-stage speech recognition result for the
given one of the plurality of pairs.
5. The method of claim 1, wherein building the classifier comprises
generating a plurality of clusters, each of the plurality of
clusters comprising at least one partial set of words, each of the
at least one partial set of words including a set of words from the
beginning of the speech recognition result to the end of question
part of the speech recognition result.
6. The method of claim 5, wherein in response to receiving the
recognition-stage speech recognition result incrementally in a word
by word manner as an input, the classifier calculates a similarity
between the input and the plurality of clusters and returns the
corresponding end of question part for the recognition-stage speech
recognition result based the similarity between the input and any
of the plurality of clusters being less than a predetermined
threshold.
7. The method of claim 5, wherein the plurality of clusters are
generated by grouping together end of question parts that include
the same or similar words.
8. The method of claim 1, wherein for a given pair from among the
plurality of pairs, the end of question part in the training-stage
speech recognition result for the given pair is determined by
checking the respective rank for the answer label included in the
given pair against the respective rank for the answer label
included in other ones of the plurality of pairs.
9. The method of claim 1, wherein a first encountered word in the
training-stage speech recognition result that results in a correct
answer label for the training stage speech recognition result is
determined as the end of question part in the training-stage speech
recognition result.
10. The method of claim 1, wherein said obtaining step comprises
generating a data structure that correlates an occurrence number of
a word in a word sequence to the rank for the word in the word
sequence.
11. A computer program product for building a classifier, the
computer program product comprising a non-transitory computer
readable storage medium having program instructions embodied
therewith, the program instructions executable by a computer to
cause the computer to perform a method comprising: preparing, by a
processor, a plurality of pairs for an information retrieval task,
each of the plurality of pairs including (i) a training-stage
speech recognition result for a respective sequence of training
words and (ii) an answer label corresponding to the training-stage
speech recognition result; obtaining, by the processor using a
search engine, a respective rank for the answer label included in
each of the plurality of pairs to obtain a set of ranks;
determining, by the processor, for each of the plurality of pairs,
an end of question part in the training-stage speech recognition
result based on the set of ranks; building, by the processor, the
classifier such that the classifier receives a recognition-stage
speech recognition result and returns a corresponding end of
question part for the recognition-stage speech recognition result,
based on the end of question part determined for the plurality of
pairs; splitting, by the processor, the recognition-stage speech
recognition result at the corresponding end of question part of the
recognition-stage speech recognition result; generating, by the
processor, a set of answer candidates for replying to the
corresponding end of question part for the recognition-stage speech
recognition result; and providing, by a display device, the set of
answer candidates to a user.
12-13. (canceled)
14. The computer program product of claim 11, wherein building the
classifier comprises generating a plurality of clusters, each of
the plurality of clusters comprising at least one partial set of
words, each of the at least one partial set of words including a
set of words from the beginning of the speech recognition result to
the end of question part of the speech recognition result.
15. The computer program product of claim 14, wherein in response
to receiving the recognition-stage speech recognition result
incrementally in a word by word manner as an input, the classifier
calculates a similarity between the input and the plurality of
clusters and returns the corresponding end of question part for the
recognition-stage speech recognition result based the similarity
between the input and any of the plurality of clusters being less
than a predetermined threshold.
16. The computer program product of claim 11, wherein for a given
pair from among the plurality of pairs, the end of question part in
the training-stage speech recognition result for the given pair is
determined by checking the respective rank for the answer label
included in the given pair against the respective rank for the
answer label included in other ones of the plurality of pairs.
17. The computer program product of claim 11, wherein a first
encountered word in the training-stage speech recognition result
that results in a correct answer label for the training stage
speech recognition result is determined as the end of question part
in the training-stage speech recognition result.
18. The computer program product of claim 11, wherein said
obtaining step comprises generating a data structure that
correlates an occurrence number of a word in a word sequence to the
rank for the word in the word sequence.
19. A system, comprising: a processor, configured to: prepare a
plurality of pairs for an information retrieval task, each of the
plurality of pairs including (i) a training-stage speech
recognition result for a respective sequence of training words and
(ii) an answer label corresponding to the training-stage speech
recognition result; obtain, using a search engine, a respective
rank for the answer label included in each of the plurality of
pairs to obtain a set of ranks; determine for each of the plurality
of pairs, an end of question part in the training-stage speech
recognition result based on the set of ranks; build a classifier
that receives a recognition-stage speech recognition result and
returns a corresponding end of question part for the
recognition-stage speech recognition result, based on the end of
question part determined for the plurality of pairs; split the
recognition-stage speech recognition result at the corresponding
end of question part of the recognition-stage speech recognition
result; and generate a set of answer candidates for replying to the
corresponding end of question part for the recognition-stage speech
recognition result; and a display device for providing the set of
answer candidates to a user.
20-21. (canceled)
22. A classifier for detecting an end of question part in speech
recognition results, the classifier comprising; a storage unit for
storing a plurality of clusters, each of the plurality of clusters
comprising at least one partial set of words, each of the partial
set of words being a set of words from a beginning of a
training-stage speech recognition result to the end of question
part of the training-stage speech recognition result; a processor
unit for calculating, in response to receiving a recognition-stage
speech recognition result incrementally in a word by word manner as
an input, a similarity between the input and the plurality of
clusters; and outputting the end of question part for the
recognition-stage speech recognition result based on the similarity
between the input and any of the plurality of clusters being less
than a predetermined threshold; splitting the recognition-stage
speech recognition result at the corresponding end of question part
of the recognition-stage speech recognition result; generating a
set of answer candidates for replying to the corresponding end of
question part for the recognition-stage speech recognition result;
and a display device for providing the set of answer candidates to
a user.
23-24. (canceled)
25. A method for detecting an end of question part in speech
recognition results, the method comprising; storing, in a storage
unit, a plurality of clusters, each of the plurality of clusters
comprising at least one partial set of words, each of the partial
set of words being a set of words from a beginning of a
training-stage speech recognition result to the end of question
part of the training-stage speech recognition result; calculating,
by a processor, in response to receiving a recognition-stage speech
recognition result incrementally in a word by word manner as an
input, a similarity between the input and the plurality of
clusters; and outputting, by the processor, the end of question
part for the recognition-stage speech recognition result based on
the similarity between the input and any of the plurality of
clusters being less than a predetermined threshold; splitting, by
the processor, the recognition-stage speech recognition result at
the corresponding end of question part for the recognition-stage
speech recognition result; generating, by the processor, a set of
answer candidates for replying to the corresponding end of question
part for the recognition-stage speech recognition result; and
providing, by a display device, the set of answer candidates to a
user.
Description
BACKGROUND
Technical Field
[0001] The present invention relates generally to information
processing and, in particular, to splitting utterances for quick
responses.
Description of the Related Art
[0002] Current speech recognition systems can accept a voice stream
and return a transcript in a timely manner with low latency.
However, when a first person talks to a second person, the second
person often times does not wait until the end of the first
person's utterance before speaking themselves, because people often
guess their response to a perceived utterance and react
speculatively. Thus, using the preceding analogy, if a computer
interaction system can react without waiting until the end of
utterances, the system would be closer to acting like a person.
Hence, there is need for a computer-based approach for providing a
quick response to an utterance.
SUMMARY
[0003] According to an aspect of the present invention, a method is
provided for building a classifier. The method includes preparing,
by a processor, a plurality of pairs for an information retrieval
task. Each of the plurality of pairs includes (i) a training-stage
speech recognition result for a respective sequence of training
words and (ii) an answer label corresponding to the training-stage
speech recognition result. The method further includes obtaining,
by the processor using a search engine, a respective rank for the
answer label included in each of the plurality of pairs to obtain a
set of ranks. The method also includes determining, by the
processor, for each of the plurality of pairs, an end of question
part in the training-stage speech recognition result based on the
set of ranks. The method additionally includes building, by the
processor, the classifier such that the classifier receives a
recognition-stage speech recognition result and returns a
corresponding end of question part for the recognition-stage speech
recognition result, based on the end of question part determined
for the plurality of pairs.
[0004] According to another aspect of the present invention, a
computer program product is provided for building a classifier. The
computer program product includes a non-transitory computer
readable storage medium having program instructions embodied
therewith. The program instructions are executable by a computer to
cause the computer to perform a method. The method includes
preparing, by a processor, a plurality of pairs for an information
retrieval task. Each of the plurality of pairs includes (i) a
training-stage speech recognition result for a respective sequence
of training words and (ii) an answer label corresponding to the
training-stage speech recognition result. The method further
includes obtaining, by the processor using a search engine, a
respective rank for the answer label included in each of the
plurality of pairs to obtain a set of ranks. The method also
includes determining, by the processor, for each of the plurality
of pairs, an end of question part in the training-stage speech
recognition result based on the set of ranks. The method
additionally includes building, by the processor, the classifier
such that the classifier receives a recognition-stage speech
recognition result and returns a corresponding end of question part
for the recognition-stage speech recognition result, based on the
end of question part determined for the plurality of pairs.
[0005] According to yet another aspect of the present invention, a
system is provided. The system includes a processor. The processor
is configured to prepare a plurality of pairs for an information
retrieval task. Each of the plurality of pairs includes (i) a
training-stage speech recognition result for a respective sequence
of training words and (ii) an answer label corresponding to the
training-stage speech recognition result. The processor is further
configured to obtain, using a search engine, a respective rank for
the answer label included in each of the plurality of pairs to
obtain a set of ranks. The processor is also configured to
determine for each of the plurality of pairs, an end of question
part in the training-stage speech recognition result based on the
set of ranks. The processor is additionally configured to build a
classifier that receives a recognition-stage speech recognition
result and returns a corresponding end of question part for the
recognition-stage speech recognition result, based on the end of
question part determined for the plurality of pairs.
[0006] According to still another aspect of the present invention,
a classifier is provided for detecting an end of question part in
speech recognition results. The classifier includes a storage unit
for storing a plurality of clusters. Each of the plurality of
clusters includes at least one partial set of words. Each of the
partial set of words is a set of words from a beginning of a
training-stage speech recognition result to the end of question
part of the training-stage speech recognition result. The
classifier further includes a processor unit for calculating, in
response to receiving a recognition-stage speech recognition result
incrementally in a word by word manner as an input, a similarity
between the input and the plurality of clusters. The classifier
also includes an output unit for outputting the end of question
part for the recognition-stage speech recognition result based on
the similarity between the input and any of the plurality of
clusters being less than a predetermined threshold.
[0007] According to still yet another aspect of the present
invention, a method is provided for detecting an end of question
part in speech recognition results. The method includes storing, in
a storage unit, a plurality of clusters. Each of the plurality of
clusters includes at least one partial set of words. Each of the
partial set of words is a set of words from a beginning of a
training-stage speech recognition result to the end of question
part of the training-stage speech recognition result. The method
further includes calculating, by a processor, in response to
receiving a recognition-stage speech recognition result
incrementally in a word by word manner as an input, a similarity
between the input and the plurality of clusters. The method also
includes outputting, by the processor, the end of question part for
the recognition-stage speech recognition result based on the
similarity between the input and any of the plurality of clusters
being less than a predetermined threshold.
[0008] These and other features and advantages will become apparent
from the following detailed description of illustrative embodiments
thereof, which is to be read in connection with the accompanying
drawings.
BRIEF DESCRIPTION OF DRAWINGS
[0009] The following description will provide details of preferred
embodiments with reference to the following figures wherein:
[0010] FIG. 1 shows an exemplary processing system to which the
present invention may be applied, in accordance with an embodiment
of the present invention;
[0011] FIG. 2 shows an exemplary automatic speech recognition
system (ASR), in accordance with an embodiment of the present
invention;
[0012] FIG. 3 shows an exemplary operating environment to which the
present invention can be applied, in accordance with an embodiment
of the present invention;
[0013] FIGS. 4-5 show an exemplary method for splitting utterances
for quick responses, in accordance with an embodiment of the
present invention;
[0014] FIG. 6 shows an exemplary graph for determining End Of
Question (EOQ) parts, in accordance with an embodiment of the
present invention;
[0015] FIG. 7 shows an exemplary cloud computing environment, in
accordance with an embodiment of the present invention; and
[0016] FIG. 8 shows an exemplary set of functional abstraction
layers provided by the cloud computing environment shown in FIG. 7,
in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION
[0017] The present invention are directed to splitting utterances
for quick responses.
[0018] In an embodiment, the present invention provides an agent
supporting system using a real-time speech recognition system that
can show a list of appropriate answer candidates to customers'
questions.
[0019] As used herein, the term "EOQ" refers to the "End Of
Question" part of a speech recognition result. Also herein, the
terms "speech recognition result" and "decoded utterance" are used
interchangeably.
[0020] In an embodiment, the present invention detects split points
of consecutive speech recognition results that are optimized for a
search system.
[0021] In an embodiment, the present invention generates a
classifier to detect End Of Question parts in speech recognition
results using training data, detects the EOQ in an input
consecutive speech recognition result, and splits the speech
recognition result at the EOQ. In this way, a quick response can be
provided to the input consecutive speech recognition result.
[0022] FIG. 1 shows an exemplary processing system 100 to which the
invention principles may be applied, in accordance with an
embodiment of the present invention. The processing system 100
includes at least one processor (CPU) 104 operatively coupled to
other components via a system bus 102. A cache 106, a Read Only
Memory (ROM) 108, a Random Access Memory (RAM) 110, an input/output
(I/O) adapter 120, a sound adapter 130, a network adapter 140, a
user interface adapter 150, and a display adapter 160, are
operatively coupled to the system bus 102.
[0023] A first storage device 122 and a second storage device 124
are operatively coupled to system bus 102 by the I/O adapter 120.
The storage devices 122 and 124 can be any of a disk storage device
(e.g., a magnetic or optical disk storage device), a solid state
magnetic device, and so forth. The storage devices 122 and 124 can
be the same type of storage device or different types of storage
devices.
[0024] A speaker 132 is operatively coupled to system bus 102 by
the sound adapter 130. A transceiver 142 is operatively coupled to
system bus 102 by network adapter 140. A display device 162 is
operatively coupled to system bus 102 by display adapter 160.
[0025] A first user input device 152, a second user input device
154, and a third user input device 156 are operatively coupled to
system bus 102 by user interface adapter 150. The user input
devices 152, 154, and 156 can be any of a keyboard, a mouse, a
keypad, an image capture device, a motion sensing device, a
microphone, a device incorporating the functionality of at least
two of the preceding devices, and so forth. Of course, other types
of input devices can also be used, while maintaining the spirit of
the present invention. The user input devices 152, 154, and 156 can
be the same type of user input device or different types of user
input devices. The user input devices 152, 154, and 156 are used to
input and output information to and from system 100.
[0026] Of course, the processing system 100 may also include other
elements (not shown), as readily contemplated by one of skill in
the art, as well as omit certain elements. For example, various
other input devices and/or output devices can be included in
processing system 100, depending upon the particular implementation
of the same, as readily understood by one of ordinary skill in the
art. For example, various types of wireless and/or wired input
and/or output devices can be used. Moreover, additional processors,
controllers, memories, and so forth, in various configurations can
also be utilized as readily appreciated by one of ordinary skill in
the art. These and other variations of the processing system 100
are readily contemplated by one of ordinary skill in the art given
the teachings of the present invention provided herein.
[0027] Moreover, it is to be appreciated that system 200 described
below with respect to FIG. 2 is a system for implementing
respective embodiments of the present invention. Part or all of
processing system 100 may be implemented in one or more of the
elements of system 200.
[0028] Further, it is to be appreciated that processing system 100
may perform at least part of the method described herein including,
for example, at least part of method 400 of FIGS. 4-5. Similarly,
part or all of system 200 may be used to perform at least part of
method 400 of FIGS. 4-5.
[0029] FIG. 2 shows an exemplary automatic speech recognition
system (ASR) 200, in accordance with an embodiment of the present
invention.
[0030] The system 200 includes a feature extractor 210, an acoustic
model 220, a pronunciation dictionary 230, a language model 240, a
searcher 250, and a speech activity detector 260.
[0031] The searcher 250 performs a search using inputs provided
from the feature extractor 210, the acoustic model 220, the
pronunciation dictionary 230, and the language model 240 to output
one or more words representative of a decoded acoustic utterance.
While mentioned in singular form, the feature extractor 210, the
acoustic model 220, the pronunciation dictionary 230, and the
language model can each include more than one of that element. For
example, the acoustic model 220 can include multiple acoustic
models, at least two being of a different type.
[0032] In a word recognition task, given an acoustic signal
corresponding to a sequence of words X=x1, x2, . . . , xn, the
feature extractor 210 first generates a compact representation of
the input as sequence of feature vectors Y=y1, y2, . . . , yt. Some
exemplary features that can be extracted by the feature extractor
210 include, but are not limited to, signal energy, pitch, zero
crossing rate, and so forth. It is to be appreciated that the
preceding features are merely illustrative and, thus, other
features can also be extracted in accordance with the teachings of
the present invention, while maintaining the spirit of the present
invention.
[0033] The acoustic model 220, the pronunciation dictionary 230,
and the language model 240 are then used by the searcher 250 to
find the most probable word sequence X given these feature vectors.
This is done by expressing the desired probability p(X|Y) using
Bayes theorem as follows:
X ^ = arg max X p ( X Y ) = arg max X p ( Y X ) p ( X ) p ( Y )
##EQU00001##
where p(X) is the a priori probability of observing a sequence of
words in the language, independent of any acoustic evidence and is
modeled using the language model component. p(X) corresponds to the
likelihood of the acoustic features Y being generated given the
word sequence X.
[0034] The language model 240 and the acoustic model 220 can be
stochastic models trained using large amounts training data. Hidden
Markov Models (HMMs) or a hybrid combination of neural networks and
HMMs can be used to implement acoustic model 220.
[0035] For large vocabulary speech recognition, not all words have
an adequate number of acoustic examples in the training data. The
acoustic data also covers only a limited vocabulary of words.
Instead of modeling incorrect probability distributions of entire
words or utterances using limited examples, the acoustic model 220
is built for basic speech sounds. By using these basic units, the
system 200 can also recognize words without acoustic training
examples. It is to be appreciated that the basic speech sounds can
be context independent phones or context dependent phones or any
other such speech sounds.
[0036] To compute the likelihood p(Y|X), each word in the
hypothesized word sequence X is first broken down into its
constituent phones using the pronunciation dictionary 230. A single
composite acoustic model for the hypothesis is constructed by
combining individual phone HMMs. In practice, to account for the
large variability of basic speech sounds, HMMs of context dependent
speech units with continuous density output distributions can be
used. There exist efficient algorithms like the Baum-Welch
algorithm to learn the parameters of the acoustic model from
training data. Neural network based acoustic models can be used
instead of, or in addition to, HMM-GMM based models.
[0037] The language model 240 generates the a priori probability
p(x). The language model 240 can be an N-gram based language
model(s), where typically bi-grams or tri-grams are used. Although
p(x) is the probability of a sequence of words, N-grams model this
probability assuming the probability of any word xi depends on only
N-1 preceding words. These probability distributions are estimated
from simple frequency counts that can be directly obtained from
large amounts of text. To account for the inability to estimate
counts for all possible N-gram sequences, techniques like
discounting and back-off are used. The language model 240 can be,
but is not limited to, a Neural Network based language model and/or
a class based language model.
[0038] The speech activity detector 260 detects speech in an input
signal that includes one or more acoustic utterances uttered by a
speaker, so that the subsequent steps of speech recognition can
focus on the speech portions of the input signal.
[0039] In the embodiment shown in FIG. 2, the elements thereof are
interconnected by a bus(es)/network(s) 201. However, in other
embodiments, other types of connections can also be used. Moreover,
in an embodiment, at least one of the elements of system 200 is
processor-based. Further, while one or more elements may be shown
as separate elements, in other embodiments, these elements can be
combined as one element. The converse is also applicable, where
while one or more elements may be part of another element, in other
embodiments, the one or more elements may be implemented as
standalone elements. Moreover, one or more elements of FIG. 2 can
be implemented in a cloud configuration including, for example, in
a distributed configuration. Additionally, one or more elements in
FIG. 2 may be implemented by a variety of devices, which include
but are not limited to, Digital Signal Processing (DSP) circuits,
programmable processors, Application Specific Integrated Circuits
(ASICs), Field Programmable Gate Arrays (FPGAs), Complex
Programmable Logic Devices (CPLDs), and so forth. These and other
variations of the elements of system 200 are readily determined by
one of ordinary skill in the art, given the teachings of the
present invention provided herein, while maintaining the spirit of
the present invention. Moreover, it is to be appreciated that other
types and configurations of a speech recognition system can also be
used in accordance with the teachings of the present invention,
while maintaining the spirit of the present invention. For example,
template matching based systems using forms of dynamic time warping
can be used, a probabilistic language model could be replaced by a
rule based grammar model, and so forth. The ASR system can also be
simple recognizer just recognizing phonemes, it could be a simple
isolated word recognizer, a digit recognizer based on rules or a
large vocabulary continuous speech recognizer, the components of
which we have described. These and other types of speech
recognition systems and constituent elements are readily determined
by one of ordinary skill in the art, given the teachings of the
present invention provided herein, while maintaining the spirit of
the present invention.
[0040] FIG. 3 shows an exemplary operating environment 300 to which
the present invention can be applied, in accordance with an
embodiment of the present invention.
[0041] The environment 300 involves a server side 310 and a client
side 350.
[0042] The server side 310 includes a speech-based computer
processing system. For illustrative purposes, the speech-based
computer processing system is an automatic speech recognition
system (ASR) 311. In an embodiment, ASR 311 can be implemented as
ASR 200 from FIG. 2. However, it is to be appreciated that block
311 can represent any speech-based computer processing system that
involves one or more of the following: speech recognition; speaker
identification; speaker verification; speaker diarisation; language
identification; keyword spotting; emotion detection; automatic
translation; court reporting; hands-free computing; home
automation; mobile telephony; and so forth.
[0043] The client side 350 includes a set of workstations 351.
[0044] Users at the workstations 351 can engage in and/or otherwise
use speech recognition sessions. The speech recognition sessions
can relate, but are not limited to, customer service, voice
dialing, machine control, data searching, data entry,
system/facility/entity access, and so forth.
[0045] Communications between the server side 310 and the client
side 350 are made through one or more networks 399.
[0046] FIGS. 4-5 show an exemplary method 400 for splitting
utterances for quick responses, in accordance with an embodiment of
the present invention.
[0047] At step 410, generate a classifier for detecting the End Of
Question (EOQ) parts in decoded utterances.
[0048] In an embodiment, step 410 includes steps 410A-410D.
[0049] At step 410A, provide an input training dataset for use in
generating the classifier. In an embodiment, the training data is a
set of combinations, where each combination include a speech
recognition result (a sequence of words) and an answer label.
[0050] At step 410B, apply a search system/search engine to the
input training dataset that returns a ranked answer.
[0051] At step 410C, determine the EOQ parts in the input training
dataset by checking a rank of a ranked answer against an input word
sequence from the input training dataset. In an embodiment, an EOQ
part is defined as a shortest point (e.g., closest word to the
start of the decoded utterance) from which the search system/search
engine can return the correct answer. FIG. 6 shows an exemplary
graph 600 for determining End Of Question (EOQ) parts, in
accordance with an embodiment of the present invention. In the
graph, the x-axis denotes the number of the input word, and the
y-axis denotes the rank of the answer for the input word. An EOQ
part is denoted by the reference numeral 610.
[0052] At step 410D, generate the EOQ classifier. In an embodiment,
the EOQ classifier is generated using a set of word sequences,
starting from the beginning of each of the word sequences to the
EOQ part of each of the word sequences.
[0053] In an embodiment, step 410D includes step 410D1.
[0054] At step 410D1, generate a set of clusters. Each of the
clusters includes at least one partial set of words. Each of the
partial set of words includes a set of words from the beginning of
the speech recognition result to the EOQ part of the speech
recognition result. In an embodiment, the clusters include a
Bag-Of-Words (BOW). In an embodiment, K-means clustering is used.
However, it is to be appreciated that the present principles are
not limited to any specific clustering technique and, thus, any
clustering technique can be used, while maintaining the spirit of
the present principles.
[0055] At step 420, apply the EOQ classifier to input data
(hereinafter interchangeably referred to as "recognition-stage
speech recognition result") to determine the EOQ part for the input
data.
[0056] In an embodiment, step 420 includes step 420A.
[0057] At step 420A, in response to receiving the recognition-stage
speech recognition result incrementally in a word by word manner as
an input, calculate (by the classifier) a similarity between the
input and the set of clusters and returns the corresponding EOQ
part for the recognition-stage speech recognition result based the
similarity between the input and any of the clusters in the set
being less than a predetermined threshold.
[0058] At step 430, split the recognition-stage speech recognition
result at the corresponding EOQ part of the recognition-stage
speech recognition result.
[0059] At step 440, generate a set of answer candidates for
replying to the EOQ part. The set of answer candidates can be
generated based on information retrieval techniques or natural
language question answering techniques. The candidates can be given
as a set of answer documents with confidence scores that correspond
to a given speech recognition result of the EOQ part. The
confidence value is calculated by comparing the speech recognition
result and each of answer documents. For example, cosine similarity
using tf-idf value of the documents or cosign similarity using
dimensionality reduced documents are used. It is to be appreciated
that the set of answer candidates can be provided immediately upon
the detection of the EOQ part.
[0060] The following are sample utterances annotated to include
"<EOQ>", where "<EOQ>" denotes the End of Question part
of an utterance.
[0061] For example, it would be useful if the system return answers
at the End Of Question part (<EOQ>).
[0062] Example 1: I want to change the password <EOQ>, and
what I should to change it?
[0063] Example 2: Would you re-issue my cash card? Can you accept
by this phone call <EOQ> or I have to go to the branch?
[0064] Example 3: I use internet banking. Tell me upper limit
<EOQ> of the amount of money transfer per day.
[0065] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0066] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0067] Characteristics are as follows:
[0068] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0069] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0070] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0071] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0072] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0073] Service Models are as follows:
[0074] Software as a Service (SaaS); the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0075] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0076] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0077] Deployment Models are as follows:
[0078] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0079] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0080] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0081] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0082] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0083] Referring now to FIG. 7, illustrative cloud computing
environment 750 is depicted. As shown, cloud computing environment
750 includes one or more cloud computing nodes 710 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 754A,
desktop computer 754B, laptop computer 754C, and/or automobile
computer system 754N may communicate. Nodes 710 may communicate
with one another. They may be grouped (not shown) physically or
virtually, in one or more networks, such as Private, Community,
Public, or Hybrid clouds as described hereinabove, or a combination
thereof. This allows cloud computing environment 750 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 754A-N shown in FIG. 7 are intended to be illustrative only
and that computing nodes 710 and cloud computing environment 750
can communicate with any type of computerized device over any type
of network and/or network addressable connection (e.g., using a web
browser).
[0084] Referring now to FIG. 8, a set of functional abstraction
layers provided by cloud computing environment 750 (FIG. 7) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 8 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0085] Hardware and software layer 860 includes hardware and
software components. Examples of hardware components include:
mainframes 861; RISC (Reduced Instruction Set Computer)
architecture based servers 862; servers 863; blade servers 864;
storage devices 865; and networks and networking components 866. In
some embodiments, software components include network application
server software 867 and database software 868.
[0086] Virtualization layer 870 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 871; virtual storage 872; virtual networks 873,
including virtual private networks; virtual applications and
operating systems 874; and virtual clients 875.
[0087] In one example, management layer 880 may provide the
functions described below. Resource provisioning 881 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 882 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 883 provides access to the cloud computing environment for
consumers and system administrators. Service level management 884
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 885 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0088] Workloads layer 890 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 891; software development and
lifecycle management 892; virtual classroom education delivery 893;
data analytics processing 894; transaction processing 895; and
splitting utterances for quick responses 896.
[0089] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0090] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0091] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0092] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0093] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0094] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0095] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0096] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0097] Reference in the specification to "one embodiment" or "an
embodiment" of the present invention, as well as other variations
thereof, means that a particular feature, structure,
characteristic, and so forth described in connection with the
embodiment is included in at least one embodiment of the present
invention. Thus, the appearances of the phrase "in one embodiment"
or "in an embodiment", as well any other variations, appearing in
various places throughout the specification are not necessarily all
referring to the same embodiment.
[0098] It is to be appreciated that the use of any of the following
"/", "and/or", and "at least one of", for example, in the cases of
"A/B", "A and/or B" and "at least one of A and B", is intended to
encompass the selection of the first listed option (A) only, or the
selection of the second listed option (B) only, or the selection of
both options (A and B). As a further example, in the cases of "A,
B, and/or C" and "at least one of A, B, and C", such phrasing is
intended to encompass the selection of the first listed option (A)
only, or the selection of the second listed option (B) only, or the
selection of the third listed option (C) only, or the selection of
the first and the second listed options (A and B) only, or the
selection of the first and third listed options (A and C) only, or
the selection of the second and third listed options (B and C)
only, or the selection of all three options (A and B and C). This
may be extended, as readily apparent by one of ordinary skill in
this and related arts, for as many items listed.
[0099] Having described preferred embodiments of a system and
method (which are intended to be illustrative and not limiting), it
is noted that modifications and variations can be made by persons
skilled in the art in light of the above teachings. It is therefore
to be understood that changes may be made in the particular
embodiments disclosed which are within the scope of the invention
as outlined by the appended claims. Having thus described aspects
of the invention, with the details and particularity required by
the patent laws, what is claimed and desired protected by Letters
Patent is set forth in the appended claims.
* * * * *