Large-margin minimum classification error training: A theoretical risk minimization perspective
Computer Speech and Language
EURASIP Journal on Audio, Speech, and Music Processing
Personalized ranking model adaptation for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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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.