Combination of agglomerative and sequential clustering for speaker diarization

This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very...

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Bibliographic Details
Main Authors: Vijayasenan, D., Valente, F., Bourlard, H.
Format: Conference Proceeding
Language:English
Subjects:
agglomerative and sequential information bottleneck
Bayesian methods
Clustering algorithms
Hidden Markov models
Meetings data
Mutual information
Parameter estimation
Parametric statistics
Partitioning algorithms
Purification
Speaker Diarization
Speech
Streaming media
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Summary:This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by 10% w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of 5% is obtained using a combination of agglomerative and sequential clustering.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2008.4518621