Maximum a posteriori linear regression for hidden Markov model adaptation

In the past few years, transformation-based model adaptation techniques have been widely used to help reducing acoustic mismatch between training and testing conditions of automatic speech recognizers. The estimation of the transformation parameters is usually carried out using estimation paradigms based on classical statistics such as maximum likelihood, mainly because of their conceptual and computational simplicity. However, it appears necessary to introduce some constraints on the possible values of the transformation parameters to avoid getting unreasonable estimates that might perturb the underlying structure of the acoustic space. In this paper, we propose to introduce such constraints using Bayesian statistics, where a prior distribution of the transformation parameters is used. A Bayesian counterpart of the well known maximum likelihood linear regression (MLLR) adaption is formulated based on maximum a posteriori (MAP) estimation. Supervised, unsupervised and incremental non-native speaker adaptation experiments are carried out to compare the proposed MAPLR approach to MLLR. Experimental results show that MAPLR outperforms MLLR.