Abstract
Maximum likelihood estimation is commonly used as a training method of HMMs for speech recognition. Corrective training and several other discriminative training methods have been proposed in recent years in order to obtain more discriminative ability of HMMs than maximum likelihood estimation. This paper describes a new discriminative HMM learning algorithm which attempts to minimize an error function on all training data. The error function is so defined as to represent a “degree” of recognition error for training data. This algorithm searches optimal HMMs by perturbing HMM parameters iteratively. It is designated “A-learning” algorithm in this paper. It is applicable not only to discrete HMMs but also to continuous HMMs. It is experimentally shown that this algorithm yields better recognition results than the corrective training algorithm for 17 Japanese consonants.
