Speech analysis by pole-zero decomposition of short-time spectra

Abstract

A new method for representation of speech spectra based on a pole-zero decomposition technique is proposed in this paper. In this method the parameters of a pole-zero model for the smoothed short-time spectrum of speech are determined by adopting a cepstral matching criterion. The cepstral coefficients of the impulse response of the model are equal to the cepstral coefficients of the signal up to a specified number which determine the order of the model system. This is analogous to autocorrelation matching in linear prediction analysis. It is shown that the model spectrum represents both peaks and valleys of the smoothed spectrum equally well, unlike the all pole model of linear prediction analysis where only the peaks are well represented. The pole and zero parameters are derived in an identical manner by approximately deconvolving the pole and zero contributions in the cepstral domain. The residual from the inverse pole-zero system can be used to obtain information about the excitation signal.