Spectral linear prediction: Properties and applications

Abstract

Linear prediction (LP) is presented as a spectral modeling technique in which the signal spectrum is modeled by an all-pole spectrum. The method allows for arbitrary spectral shaping in the frequency domain, and for modeling of continuous as well as discrete spectra (such as filter bank spectra). In addition, using the method of selective linear, prediction, all-pole modeling is applied to selected portions of the spectrum, with applications to speech recognition and speech compression. LP is compared with traditional analysis-by-synthesis (AbS) techniques for spectral modeling. It is found that linear prediction offers computational advantages over AbS, as well as better modeling properties if the variations of the signal spectrum from the desired spectral model are large. For relatively smooth spectra and for filter bank spectra, AbS is judged to give better results. Finally, a sub-optimal solution to the problem of all-zero modeling using LP is given.