Variational Bayesian Adaptation for Speaker Clustering
Fabio Valente, Christian J. Wellekens · 2006
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 MAP-BIC method.