|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
FULLY VECTOR-QUANTIZED NEURAL NETWORK-BASED CODE-EXCITED NONLINEAR PREDICTIVE SPEECH CODING
Lizhong Wu, Mahesan Niranjan and Frank Fallside
March 1992, revised May 1993
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 take 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 quantisers, (b) the analysis of the output variance of the nonlinear predictive filter to an input disturbance, and (c) the design of nonlinear predictive speech coders. The above studies have resulted in a fully vector-quantised, 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 to speech coding and the improvement in coding performance.
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