Discrete all-pole modeling

A new method is introduced for parametric modeling of spectral envelopes when only a discrete set of spectral points is given. This method, which we call discrete all-pole (DAP) modeling, uses a discrete version of the Itakura-Saito distortion measure as its error criterion. One result is a new autocorrelation matching condition that overcomes the limitations of linear prediction and produces better fitting spectral envelopes for spectra that are representable by a relatively small discrete set of values, such as in voiced speech. We present an iterative algorithm for DAP modeling that is shown to converge to a unique global minimum. We also present results of applying DAP modeling to real and synthetic speech. DAP modeling is extended to allow frequency-dependent weighting of the error measure, so that spectral accuracy can be enhanced in certain frequency regions relative to others.