An improved algorithm for noise-robust sparse linear prediction of speech

作     者：ZHOU Bin ZOU Xia ZHANG Xiongwei 

作者机构：The 63rd Research Institute of PLA General Staff Headquarters PLA University of Science and Technology 

出 版 物：《Chinese Journal of Acoustics》 (声学学报（英文版）)

年 卷 期：2015年第34卷第1期

页      面：84-95页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 

基　　金：supported by the Natural Science Foundation of Jiangsu Province(BK2012510,BK20140074) the National Postdoctoral Foundation of China(20090461424) 

主　　题：An improved algorithm for noise-robust sparse linear prediction of speech PESQ LP 

摘      要：The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.