MAXIMUM MUTUAL INFORMATION ESTIMATION OF HIDDEN MARKOV MODEL PARAMETERS FOR SPEECH RECOGNITION.

Abstract

A method for estimating the parameters of hidden Markov models of speech is described. Parameter values are chosen to maximize the mutual information between an acoustic observation sequence and the corresponding word sequence. Recognition results are presented, comparing this method with maximum-likelihood estimation. In the example given, estimating parameters by maximizing mutual information resulted in the training script having a probability 10**1 **8 **9 times greater than when parameters were estimated by maximum likelihood estimation. Moreover, training by maximizing mutual information resulted in 18% fewer recognition errors.