Bayes factor based speaker clustering for speaker diarization
| Data(s) |
01/05/2010
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| Resumo |
This paper proposes the use of the Bayes Factor to replace the Bayesian Information Criterion (BIC) as a criterion for speaker clustering within a speaker diarization system. The BIC is one of the most popular decision criteria used in speaker diarization systems today. However, it will be shown in this paper that the BIC is only an approximation to the Bayes factor of marginal likelihoods of the data given each hypothesis. This paper uses the Bayes factor directly as a decision criterion for speaker clustering, thus removing the error introduced by the BIC approximation. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, leading to a 14.7% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system. |
| Formato |
application/pdf |
| Identificador | |
| Publicador |
IEEE |
| Relação |
http://eprints.qut.edu.au/31414/1/c31414.pdf http://www.isspa2010.com/ Wang, David, Vogt, Robbie, & Sridharan, Sridha (2010) Bayes factor based speaker clustering for speaker diarization. In Proceedings of 10th International Conference on Information Science, Signal Processing and their Applications, IEEE, Renaissance Hotel, Kuala Lumpur. http://purl.org/au-research/grants/ARC/LP0991238 |
| Direitos |
Copyright 2010 IEEE Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
| Fonte |
Faculty of Built Environment and Engineering; Information Security Institute; School of Engineering Systems |
| Palavras-Chave | #090609 Signal Processing #Bayes Factor #Bayesian Information Criterion #Speaker Clustering #Speaker Diarization |
| Tipo |
Conference Paper |
