MMI training for continuous phoneme recognition on the TIMIT database

Experiences with a phoneme recognition system for the TIMIT database which uses multiple mixture continuous-density monophone HMMs (hidden Markov models) trained using MMI (maximum mutual information) is reported. A comprehensive set of results are presented comparing the ML (maximum likelihood) and MMI training criteria for both diagonal and full covariance models. These results using simple monophone HMMs show that clear performance gains are achieved by MMI training. These results are comparable with the best reported by others, including those which use context-dependent models. In addition, a number of performance and implementation issues which are crucial to successful MMI training are discussed.<>