ICSLP 2004, 8th International Symposium on Chinese Spoken Language Processing, October 4-8, 2004, Jeju Island, Korea
This paper aims at comparing the Bayesian Information Criterion and the Variational Bayesian approach for scoring unknown multiple speaker clustering. Variational Bayesian learning is a very effective method that allows parameter learning and model selection at the same time. The application we consider here consists in finding the optimal clustering in a conversation where the speaker number is not a priori known. Experiments are run on synthetic data and on the evaluation data set NIST-1996 HUB-4. VB learning achieves higher score in terms of average cluster purity and average speaker purity compared to ML/BIC.
Type:
Conference
City:
Jeju Island
Date:
2004-10-08
Department:
Digital Security
Eurecom Ref:
1458
Copyright:
© ISCA. Personal use of this material is permitted. The definitive version of this paper was published in ICSLP 2004, 8th International Symposium on Chinese Spoken Language Processing, October 4-8, 2004, Jeju Island, Korea and is available at : http://dx.doi.org/10.21437/Interspeech.2004-248
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