Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding

Recent studies have shown that nonlinear predictors can achieve about 2-3 dB improvement in speech prediction over conventional linear predictors. In this paper, we exploit the advantage of the nonlinear prediction capability of neural networks and apply it to the design of improved predictive speech coders. Our studies concentrate on the following three aspects: (a) the development of short-term (formant) and long-term (pitch) nonlinear predictive vector quantizers (b) the analysis of the output variance of the nonlinear predictive filter with respect to the input disturbance (c) the design of nonlinear predictive speech coders. The above studies have resulted in a fully vector-quantized, code-excited, nonlinear predictive speech coder. Performance evaluations and comparisons with linear predictive speech coding are presented. These tests have shown the applicability of nonlinear prediction in speech coding and the improvement in coding performance