论文标题
稀疏线性预测的最大相模型
Maximum Phase Modeling for Sparse Linear Prediction of Speech
论文作者
论文摘要
线性预测（LP）是语音处理中无处不在的分析方法。各种研究都通过将稀疏性约束引入LP框架来集中在稀疏的LP算法上。稀疏LP已被证明在与语音建模和编码有关的几个问题中有效。但是，所有现有方法都假设语音信号为最小相。由于已知语音是混合相的，因此所得的残留信号包含持续的最大相分量。本文的目的是提出一种新型技术，该技术结合了语音的最大相相贡献的建模，并可以应用于任何滤波器表示。所提出的方法显示出可显着增加LP残差信号的稀疏性，并在两个说明性应用中有效：语音极性检测和激发模型。
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.
