Diarization of Telephone Conversations Using Factor Analysis

We report on work on speaker diarization of telephone conversations which was begun at the Robust Speaker Recognition Workshop held at Johns Hopkins University in 2008. Three diarization systems were developed and experiments were conducted using the summed-channel telephone data from the 2008 NIST...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2010-12, Vol.4 (6), p.1059-1070
Main Authors: Kenny, Patrick, Reynolds, Douglas, Castaldo, Fabio
Format: Article
Language:English
Subjects:
Adaptation model
Agglomeration
Bayesian analysis
Bayesian methods
Channel factors
Clustering
Clustering methods
Conversation
diarization
Errors
Guidelines
Hidden Markov models
Reduction
speaker factors
Speaker recognition
speaker segmentation
Speech
Speech recognition
Studies
Telephones
variational Bayes
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Summary:We report on work on speaker diarization of telephone conversations which was begun at the Robust Speaker Recognition Workshop held at Johns Hopkins University in 2008. Three diarization systems were developed and experiments were conducted using the summed-channel telephone data from the 2008 NIST speaker recognition evaluation. The systems are a Baseline agglomerative clustering system, a Streaming system which uses speaker factors for speaker change point detection and traditional methods for speaker clustering, and a Variational Bayes system designed to exploit a large number of speaker factors as in state of the art speaker recognition systems. The Variational Bayes system proved to be the most effective, achieving a diarization error rate of 1.0% on the summed-channel data. This represents an 85% reduction in errors compared with the Baseline agglomerative clustering system. An interesting aspect of the Variational Bayes approach is that it implicitly performs speaker clustering in a way which avoids making premature hard decisions. This type of soft speaker clustering can be incorporated into other diarization systems (although causality has to be sacrificed in the case of the Streaming system). With this modification, the Baseline system achieved a diarization error rate of 3.5% (a 50% reduction in errors).
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2010.2081790