Maximum mutual information training of a neural predictive-based HMM speech recognition system

Abstract

A corrective training scheme based on the maximum mutual information (MMI) criterion is developed for training a neural predictive-based HMM (hidden Markov model) speech recognition system. The performance of the system on speech recognition tasks when trained with this technique is compared to its performance when trained using the maximum likelihood (ML) criterion. Preliminary results obtained indicate the superiority of ML training over MMI training for predictive-based models. This result is in agreement with earlier findings in the literature regarding direct classification models.<>