Neural net nonlinear prediction for speech data

Abstract

A new, nonlinear, neural network based, predictor has been devised for the encoding of speech data. It may be used in the design of a differential pulse code modulation (DPCM) coder for speech. A hybrid neural network architecture has been employed which combines the perceptron and backpropagation paradigms, thus called the PB-hybrid (PBH). Only two neurons are needed in the backpropagation section, keeping the required overhead modest. This predictor is designed by supervised training, based on a typical sequence of digitised values of samples in a speech frame. Simulation experiments have been carried out using 15 ms frames of 16 kHz speech data. The results obtained for the prediction gain show a 3dB advantage of the PBH network over the linear predictor.