Linear regression under maximum a posteriori criterion with Markov random field prior

Abstract

Speaker adaptation using linear transformations under the maximum a posteriori (MAP) criterion has been studied in this paper. The purpose is to improve the matrix estimation in the widely used maximum likelihood linear regression (MLLR) adaptation, which might generate poorly structured transform matrices when adaptation data are sparse. Unlike traditional MAP based adaptations, many known prior distributions of HMM parameters, such as normal-Washart priors, do not have a close form solution in the transform estimation. In Markov random field linear regression (MRFLR), the prior distribution of HMM parameters is modeled by Markov random field, which leads to a close form solution of estimating the linear transforms. Experimental results show that MRFLR outperforms MLLR when adaptation data are sparse, and converges to the MLLR performances when more adaptation data are available.