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This paper describes a new duration-based state transition model that incorporates a discriminative weighting parameter. The serious problem of acoustical/temporal variation in different speakers utterances has long made it difficult to design a robust speech recognition model. To cope with this problem, we integrate state-dependent weighting parameters into the structure of the durationbased state transition model and control the score for the accompanying temporal segment. These state-dependent weightings absorb local variations in an utterance and provide discriminative ability that can directly influence the total score for the speech pattern. Moreover, a durationbased matching procedure precludes stiff dynamic warping that involves excessive time stretching or compression. We train all of the model parameters including proposed parameters, by using a minimum classification error criterion. Minimum classification error training is implemented by a generalized probabilistic descent method that can directly minimize the number of recognition errors between competing models. We also discuss the relationship of this model to a conventional hidden Markov model, and applicability of the minimum-classification-error/generalizedprobabilistic-descent training to the hidden Markov model. The experimental results of spotting-based speaker-independent word recognition show that both state-dependent weightings and duration control helped to improve recognition performance. Furthermore, for low-performance speakers, the recognition rate was raised from 65.5% to 92.0% after speaker adaptation using the training, proving that the training was effective. © 2000 Scripta Technica, Syst Comp Jpn, 31(4): 98108, 2000

Discriminative state-dependent weightings of duration-based state transition model through minimum classification error training