LARGE MARGIN TRAINING OF SEMI-MARKOV MODEL FOR PHONETIC RECOGNITION

This paper considers a large margin training of semi-Markov model (SMM) for phonetic recognition. The SMM framework is better suited for phonetic recognition than the hidden Markov model (HMM) framework in that the SMM framework is capable of simultaneously segmenting the uttered speech into phones and labeling the segment-based features. In this paper, the SMM framework is used to define a discriminant function that is linear in the joint feature map which attempts to capture the long-range statistical dependencies within a segment and between adjacent segments of variable length. The parameters of the discriminant function are estimated by a large margin learning criterion for structured prediction. The parameter estimation problem, which is an optimization problem with many margin constraints, is solved by using a stochastic subgradient descent algorithm. The proposed large margin SMM outperforms the large margin HMM on the TIMIT corpus.