Inter-speaker correlations, intra-speaker correlations and Bayesian adaptation

There are two types of prior distribution that can be viewed as natural for extended MAP (or EMAP) speaker adaptation. One arises from modeling the correlations between speakers (assumed to be constant across HMM Gaussians) and the other from modeling the correlations between HMM Gaussians (assumed to be constant across speakers). In this paper we present new results establishing the usefulness of correlations of the first type for speaker adaptation and we outline a tensor product construction which enables both types of correlation to be integrated in a common mathematical framework. We also present the results of some experiments which suggest that the two types of correlation are equally effective for speaker adaptation and that there is no incremental improvement to be gained by modeling both of them simultaneously.