Phoneme recognition by means of predictive neural networks

Abstract

In this paper we present a phoneme recognition system based on predictive neural networks. Both feed-forward and recurrent neural networks are used for the prediction of observation vectors of speech frames. Preliminary experiments are conducted to study the discriminative quality of the prediction error as distortion measure and other similarity measures based on the Gaussian and Rayleigh distributions. The average prediction error of the neural networks is interpreted as a new feature generated by the neural net through nonlinear feature transformation. The proposed system is evaluated on a continuous speech phoneme recognition task. The recognition results that we obtain with the proposed neural network based system are compared with results obtained by a continuous density HMM system.