Maximum Phase Modeling for Sparse Linear Prediction of Speech

TL;DR

This paper introduces a novel maximum-phase modeling technique for sparse linear prediction of speech, addressing the limitation of minimum-phase assumptions and improving sparsity and application effectiveness.

Contribution

It proposes a new maximum-phase modeling approach for speech, enhancing sparse LP and broadening its applicability beyond minimum-phase assumptions.

Findings

Increases sparsity of LP residual signals

Improves speech polarity detection accuracy

Enhances excitation modeling effectiveness

Abstract

Linear prediction (LP) is an ubiquitous analysis method in speech processing. Various studies have focused on sparse LP algorithms by introducing sparsity constraints into the LP framework. Sparse LP has been shown to be effective in several issues related to speech modeling and coding. However, all existing approaches assume the speech signal to be minimum-phase. Because speech is known to be mixed-phase, the resulting residual signal contains a persistent maximum-phase component. The aim of this paper is to propose a novel technique which incorporates a modeling of the maximum-phase contribution of speech, and can be applied to any filter representation. The proposed method is shown to significantly increase the sparsity of the LP residual signal and to be effective in two illustrative applications: speech polarity detection and excitation modeling.