Supervised adaptation of MCE-trained CDHMMS using minimum classification error linear regression

In this paper, we present a formulation of minimum classification error linear regression (MCELR) for adaptation of Gaussian mixture continuous density HMM (CDHMM) parameters. We demonstrate that the MCELR can be used to adapt the MCE-trained HMM parameters under a consistent criterion. In a supervised speaker adaptation application, we observe that such adapted models perform better than the ones adapted using MLLR from the ML-trained seed models. We also observe that the MCELR performs consistently better than the MLLR for either sets of seed models.