Speaker adaptation using discriminative linear regression on time-varying mean parameters in trended HMM

In this letter, we report our recent work on applications of the combined maximum likelihood linear regression (MLLR) and the minimum classification error training (MCE) approach to estimating the time-varying polynomial Gaussian mean functions in the trended hidden Markov model (HMM). We call this integrated approach the minimum classification error linear regression (MCELR), which has been developed and implemented in speaker adaptation experiments using TI46 corpora. Results show that the adaptation of linear regression on time-varying mean parameters is always better when fewer than three adaptation tokens are used.