Time-varying parametric modeling of speech

For linear predictive coding (LPC) of speech, the speech waveform is modeled as the output of an all-pole filter. The waveform is divided into many short intervals (10–30 msec) during which the speech signal is assumed to be stationary. For each interval the constant coefficients of the all-pole filter are estimated by linear prediction by minimizing a squared prediction error criterion. This paper investigates a modification of LPC, called time-varying LPC, which can be used to analyze nonstationary speech signals. In this method, each coefficient of the all-pole filter is allowed to be time-varying by assuming it is a linear combination of a set of known time functions. The coefficients of the linear combination of functions are obtained by the same least squares error technique used by the LPC. Methods are developed for measuring and assessing the performance of time-varying LPC and results are given from the time-varying LPC analysis of both synthetic and real speech.