Variational bayesian adaptation for speaker clustering

Valente, Fabio; Wellekens, Christian J
ICASSP 2005, 30th IEEE International Conference on Acoustics, Speech, and Signal Processing, March 18-23, 2005- Philadelphia, USA

In this paper we explore the use of Variational Bayesian (VB) learning for adaptation in a speaker clustering framework. Variational learning offers the interesting property of making model learning and model selection at the same time. We compare VB learning with a classical MAP/BIC (MAP for training, BIC for model selection) approach. Results on the NIST BN-96 HUB4 database show that VB learning can outperform the classical MAPBIC method.


DOI
Type:
Conférence
City:
Philadelphia
Date:
2005-03-18
Department:
Sécurité numérique
Eurecom Ref:
1569
Copyright:
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