Word-level blind diarization of recorded calls with arbitrary number of speakers
AssigneeVerint Systems Ltd.
InventorsAlex Gorodetski, Oana Sidi, Ron Wein, Ido Shapira
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Abstract
Disclosed herein are methods of diarizing audio data using first-pass blind diarization and second-pass blind diarization that generate speaker statistical models, wherein the first pass-blind diarization is on a per-frame basis and the second pass-blind diarization is on a per-word basis, and methods of creating acoustic signatures for a common speaker based only on the statistical models of the speakers in each audio session.
Claims20
A method of blind diarization of audio data having a first-pass blind diarization process and a second-pass blind diarization process, the method comprising:
- identifying non-speech segments in audio data and segmenting the audio data into a plurality of utterance that are separated by the identified non-speech segments,
- representing each utterance as an utterance model representative of a plurality of feature vectors of each utterance;
- clustering the utterance models;
- and
- constructing a plurality of speaker models from the clustered utterance models.
A system comprising:
- at least one processor;
- and
- at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
- identify non-speech segments in audio data and segmenting the audio data into a plurality of utterance that are separated by the identified non-speech segments,
- represent each utterance as an utterance model representative of a plurality of feature vectors of each utterance;
- cluster the utterance models;
- and
- construct a plurality of speaker models from the clustered utterance models.
A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to:
- identify non-speech segments in audio data and segmenting the audio data into a plurality of utterance that are separated by the identified non-speech segments,
- represent each utterance as an utterance model representative of a plurality of feature vectors of each utterance;
- cluster the utterance models;
- and
- construct a plurality of speaker models from the clustered utterance models.
Dependencies are inferred from the claim text.