HYBRID NEURAL NETWORK/HIDDEN MARKOV MODEL SYSTEMS FOR CONTINUOUS SPEECH RECOGNITION

Abstract

MultiLayer Perceptrons (MLP) are an effective family of algorithms for the smooth estimation of highly-dimensioned probability density functions that are useful in continuous speech recognition. Hidden Markov Models (HMM) provide a structure for the mapping of a temporal sequence of acoustic vectors to a generating sequence of states. For HMMs that are independent of phonetic context, the MLP approaches have consistently provided significant improvements (once we learned how to use them). Recently, these results have been extended to context-dependent models. In this paper, after having reviewed the basic principles of our hybrid HMM/MLP approach, we describe a series of experiments with continuous speech recognition. The hybrid methods directly trade off computational complexity for reduced requirements of memory and memory bandwidth. Results are presented on the widely used Resource Management speech database that is distributed by the National Institute of Standards and Technology. These results demonstrate performance that is at least as good as any other reported continuous speech recognition system (for this task).