Phoneme recognition by phoneme filter neural networks

Abstract

A phoneme filter neural network (PFN) approach to vowel recognition is described. The PFN is a multilayer neural network with fewer hidden units than input units prepared for each of the phoneme categories. Each network is trained as identity mapping by speech data belonging to one phoneme category. In the recognition process, the similarity between the input data and output data is computed for each network. The results of an experiment involving the Japanese vowel recognition task showed that the PFN recognition rates for the top two or more choices are higher than those of a conventional three-layer neural network and the PFN outputs represented candidate likelihoods. It was also confirmed that the PFN has a mapping ability and recognition performance superior to those of the linear K-L transformation method because of the nonlinearity of the PFN.< >