Performance analysis of DPCM speech-transmission systems using Kalman predictors

Most of the DPCM systems for speech transmission, employ a digital transversal predictor. In such a way an all-pole vocal tract model is assumed; this is not always good either for a fixed or time-varying model. To overcome this drawback some recursive-filter predictors (e.g., Kalman predictors) have been proposed, but not examined for real speech. This paper develops one of these procedures, and, by exploiting an average state-space model of the vocal tract, analyzes the system performance in the case of real voice.