Task independent and dependent training: performance comparison of HMM and hybrid HMM/MLP approaches

Abstract

Compares speaker independent isolated word recognition performance obtained with standard phonemic hidden Markov models (HMMs) and hybrid approaches using a multilayer perceptron (MLP) to estimate the HMM emission probabilities. This latter approach has previously been shown particularly effective on a large vocabulary, speaker independent, continuous speech recognition task (i.e., ARPA Resource Management) by using simple context-independent phoneme models and single pronunciation word models. As a consequence, the main goal of the paper is to compare the performance which can be achieved by the different approaches for both task dependent and independent training.<>