Scoring unknown speaker clustering : VB vs. BIC

This paper aims at comparing the Bayesian Information Criterion and the Variational Bayesian approach for scoring unknown multiple speakerclustering. 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.