A maximum model distance approach for HMM-based speech recognition

Abstract

This paper presents a new approach for HMM-training which is based on the maximum model distance (MMD) criterion for different similar utterances. This approach differs from the traditional maximum likelihood (ML) approach in that the ML only considers the likelihood P( O ν | λ ν ) for a single utterance, while the MMD compares the likelihood P( O ν | λ ν ) against those similar utterances and maximizes their likelihood differences. Theoretical and practical issues concerning this approach are investigated. In addition, the corrective training [Bahl, Brown, de Souza and Mercer, IEEE Trans. Speech Audio Process. 1(1), (1993)] of the MMD was also included in this paper and we proved that the corrective training proposed by Bahl et al. (1993) is a special case of our MMD approach. Both speaker-dependent and multi-speaker experiments have been carried out on the Chinese An-set syllables and also the 599 most common utterances from the TIMIT database. Experimental results showed that significant error reduction can be achieved through the proposed approach.