Blind diarization of recorded calls with arbitrary number of speakers

1. 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.

2. The method of claim 1 , further comprising applying voice activity detection to the plurality of frames to segment the audio data into the plurality of utterances.

3. The method of claim 1 , wherein the acoustic features are perceptive linear predictive analytics.

4. The method of claim 1 , further comprising:

constructing a universal Gaussian model form the feature vectors of the plurality of utterances;

wherein the GMM for each utterance is further calculated using the universal Gaussian model.

5. The method of claim 1 , further comprising updating the constructed hidden Markov model using the extracted acoustic features for the audio data.

6. The method of claim 5 , wherein the updating is a Baum-Welch re-estimation.

7. The method of claim 1 , further comprising:

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;

wherein the plurality of speaker models are constructed from the projected utterance models.

8. The method of claim 1 , wherein the audio data is streaming audio data.

9. 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.

10. The method of claim 9 , further comprising extracting at least one acoustic feature from each of the plurality of frames.

11. The method of claim 10 , wherein the acoustic features are Mel-frequency cepstral coefficients (MFCC).

12. The method of claim 11 , further comprising:

approximating a distribution of the MFCC in each utterance by calculating at least one Gaussian mixture model (GMM) for each utterance; and

calculating a distance between each GMM.

13. 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.