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.
Variational bayesian adaptation for speaker clustering
ICASSP 2005, 30th IEEE International Conference on Acoustics, Speech, and Signal Processing, March 18-23, 2005- Philadelphia, USA
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