Volterra adaptive prediction of speech with application to waveform coding

Recent studies have shown that the airflow in the vocal tract is highly unstable and oscillates between its walls. Therefore linear prediction speech analysis, which is based on laminar airflow hypothesis, leads to approximate representations. This paper deals with nonlinear speech modeling and its exploitation to high quality medium-rate coding. We first give evidence that the nonlinearities in speech can be described by a second-order finite memory Volterra operator. An algorithm for performing adaptive nonlinear prediction is described. Application of the algorithm to speech coding is then reported and stability and computational issues are discussed. Performance evaluations and comparisons with linear predictive speech coding are reported and show that improvements in coding performances can be obtained.