Constrained-stochastic-excitation coding

Abstract

In CELP coding, stochastic (noise-like) excitation is used in exciting a cascade of long-term and short-term all-pole linear synthesis filters. This approach is based on the observation that the ideal excitation, obtained by inverse-filtering the speech signal, can be modeled for simplicity as Gaussian white noise. Although such stochastic excitation resembles the ideal excitation in its global statistical properties, it contains a noisy component that is irrelevant to the synthesis process. This component introduces some roughness and noisiness in the synthesized speech. The present invention reduces this effect by adaptively controlling the level of the stochastic excitation. The proposed control mechanism links the stochastic excitation to the long-term predictor in such a way that the excitation level is inversely related to the efficiency of the predictor. As a result, during voiced sounds, the excitation level is considerably attenuated and the synthesis is mainly accomplished by exciting the short-term filter with the periodic output of the long-term filter. This reduces the noisiness, enhances the pitch structure of the synthesized speech and its perceptual quality.