An implementation of short-timed speech recognition on layered neural nets

Abstract

The authors show a new way to handle the sequential nature of speech signals in multilayer perceptrons (MLPs) or other neural net machines. A static model in the form of state transition probability matrices representing short speech units such as syllables which correspond to Chinese utterances of isolated characters were adopted and as learning patterns for MLPs. The network architecture and learning algorithms are described. Experimental results on speech recognition are included.<>