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US 9,460,722 B2Granted
Blind diarization of recorded calls with arbitrary number of speakers
AssigneeVerint Systems Ltd.
InventorsOana Sidi, Ron Wein
Life of this patent
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Abstract
In a method of diarization of audio data, audio data is segmented into a plurality of utterances. Each utterance is represented as an utterance model representative of a plurality of feature vectors. The utterance models are clustered. A plurality of speaker models are constructed from the clustered utterance models. A hidden Markov model is constructed of the plurality of speaker models. A sequence of identified speaker models is decoded.
Claims13
A method of diarization of audio data, the method comprising:
- receiving audio data;
- segmenting the audio data into a plurality of frames,
- segmenting audio data into a plurality of utterances, wherein each of the plurality of utterances comprises one or more of the plurality of frames;
- extracting at least one acoustic feature from each of the plurality of frames, wherein the acoustic features are Mel-frequency cepstral coefficients (MFCC);
- representing each utterance as an utterance model representative of the MFCC;
- approximating a distribution of the MFCC in each utterance by calculating at least one Gaussian mixture model (GMM) for each utterance;
- calculating a distance between each GMM;
- constructing an affinity matrix based upon the distances between utterances;
- computing a stochastic matrix from the affinity matrix;
- computing eigenvalues and corresponding eigenvectors for the stochastic matrix;
- embedding the utterances into multi-dimensional vectors, wherein the utterance models comprise the multi-dimensional vectors;
- clustering the utterance models;
- constructing a plurality of speaker models from the clustered utterance models;
- constructing a hidden Markov model of the plurality of speaker models;
- decoding a sequence of identified speaker models that best corresponds to the utterances of the audio data;
- and
- creating diarized audio data using the sequence of identified speaker models that best correspond to the utterances of the audio data.
14 more elements
A method of diarization of audio data, the method comprising:
- receiving audio data;
- segmenting the audio data into a plurality of frames;
- segmenting audio data into a plurality of utterances wherein each utterance of the plurality comprises more than one frame of the plurality of frames;
- representing each utterance as an utterance model representative of a plurality of feature vectors of each utterance;
- projecting the utterance models onto a lower dimensional space to create a plurality of projected utterance models, wherein in the projected utterance models, a distance between utterances is a defined metric;
- constructing an affinity matrix based upon the distances between utterances;
- computing a stochastic matrix from the affinity matrix;
- computing eigenvalues and corresponding eigenvectors for the stochastic matrix;
- and
- embedding the utterances into multi-dimensional vectors, wherein the utterance models comprise the multi-dimensional vectors;
- clustering the projected utterance models;
- constructing a plurality of speaker models from the clustered projected utterance models;
- constructing a hidden Markov model of the plurality of speaker models;
- decoding a sequence of identified speaker models that best corresponds to the utterances of the audio data;
- and
- creating diarized audio data using the sequence of identified speaker models that best correspond to the utterances of the audio data.
13 more elements
A method of diarization of audio data, the method comprising:
- receiving audio data;
- segmenting the audio data into a plurality of frames;
- segmenting audio data into a plurality of utterances wherein each utterance of the plurality comprises more than one frame of the plurality of frames;
- extracting at least one acoustic feature from each of the plurality of frames;
- representing each utterance as an utterance model representative of the extracted acoustic features of the plurality of frames of each utterance;
- approximating a distribution of the extracted acoustic features of each utterance by calculating at least one Gaussian mixture model for each utterance;
- calculating a distance between each of the Gaussian mixture models;
- constructing an affinity matric based upon the distances between utterances;
- computing a stochastic matrix from the affinity matrix;
- computing eigenvalues and corresponding eigenvectors for the stochastic matrix;
- and
- embedding the utterances into multi-dimensional vectors, wherein the utterance models comprise the multi-dimensional vectors;
- clustering the utterance models based upon the calculated distances;
- constructing a plurality of speaker models from the clustered projected utterance models;
- constructing a hidden Markov model of the plurality of speaker models;
- decoding a sequence of identified speaker models that best corresponds to the utterances of the audio data;
- and
- creating diarized audio data using the sequence of identified speaker models that best correspond to the utterances of the audio data.
15 more elements
Dependencies are inferred from the claim text.