minimum classification error linear regression for acoustic model adaptation of continuous density HMMS

In this paper, a concatenated "super" string model based minimum classification error (MCE) model adaptation approach is described. We show that the error rate minimization in the proposed approach can be formulated into maximizing a special ratio of two positive functions. The proposed string model is used to derive the growth transform based error rate minimization for MCE linear regression (MCELR). It provides an effective solution to apply MCE approach to acoustic model adaptation with sparse data. The proposed MCELR approach is studied and compared with the maximum likelihood linear regression (MLLR) based model adaptation. Experiments on large vocabulary speech recognition tasks are performed. Experimental results indicate that the proposed MCELR model adaptation can lead to significant speech recognition performance improvement and its performance advantage over the MLLR based approach is observed even when the amount of adaptation data is sparse.