Testing the Hypothesis of Multivariate Normality in Bayesian Approaches to Speaker Adaptation

Bayesian approaches to speaker adaptation are popular in Automatic Speech Recognition (ASR) systems. In most kinds of Bayesian adaptation, there are parameters whose prior distributions are assumed to be multivariate normal. This paper presents a methodology, which can test the hypothesis of multivariate normality. When applied to Maximum A Posterior (MAP) adaptation, we found that the real prior distributions of the mean vectors are far from normal, which are always assumed in the MAP procedure. This result implies that better choice of the prior form may improve the adaptation result.