Minimum classification error (MCE) model adaptation of continuous density HMMS

In this paper, a framework of minimum classification error (MCE) model adaptation for continuous density HMMs is proposed based on the approach of "super" string model. We show that the error rate minimization in the proposed approach can be formulated into maximizing a special ratio of two positive functions, and from that a general growth transform algorithm is derived for MCE based model adaptation. This algorithm departs from the generalized probability descent (GPD) algorithm, and it is well suited for model adaptation with a small amount of training data. The proposed approach is applied to linear regression based variance adaptation, and the close form solution for variance adaptation using MCE linear regression (MCELR) is derived. The MCELR approach is evaluated on large vocabulary speech recognition tasks. The relative performance gain is more than doubled on the standard (WSJ Spoke 3) database, comparing to maximum likelihood linear regression (MLLR) based variance adaptation for the same amount of adaptation data.