INTERSPEECH 2004 - ICSLP
This paper aims at comparing the Bayesian Information Criterion and the Variational Bayesian approach for scoring unknown multiple speaker clustering. Variational Bayesian learning is 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.
Bibliographic reference. Valente, Fabio / Wellekens, Christian (2004): "Scoring unknown speaker clustering : VB vs. BIC", In INTERSPEECH-2004, 593-596.