Speaker-independent syllable recognition by a pyramidical neural net

Abstract

The application of the pyramidical multilayered neural net to speaker-independent recognition of isolated Chinese syllables was investigated. The feature extraction algorithm is described. Experiments involving 90 speakers from 25 provinces of China show that accuracies of 82.7% and 87.1% can be achieved, respectively, for ten isolated digits and seven typical syllables, and an over 75% cross-sex recognition rate can be obtained. The results indicate that this neural net technique can be applied to speaker-independent syllable recognition and that its performance is comparable to that of the hidden Markov model method.<>