改进的噪声鲁棒语音稀疏线性预测算法  

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

作　　者：周彬[1] 邹霞[1] 张雄伟[1] 

机构地区：[1]解放军理工大学,南京210007

出　　处：《声学学报》2014年第5期655-662,共8页Acta Acustica

基　　金：江苏省自然科学基金(BK2012510);国家博士后科研基金(20090461424)资助

摘　　要：语音线性预测分析算法在噪声环境下性能会急剧恶化,针对这一问题,提出一种改进的噪声鲁棒稀疏线性预测算法。首先采用学生t分布对具有稀疏性的语音线性预测残差建模,并显式考虑加性噪声的影响以提高模型鲁棒性,从而构建完整的概率模型。然后采用变分贝叶斯方法推导模型参数的近似后验分布,最终实现噪声鲁棒的稀疏线性预测参数估计。实验结果表明,与传统算法以及近几年提出的基于l_1范数优化的稀疏线性预测算法相比,该算法在多项指标上具有优势,对环境噪声具有更好的鲁棒性,并且谱失真度更小,因而能够有效提高噪声环境下的语音质量。The performance of linear prediction analysis of speech deteriorates rapidly under noisy environment. 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 influence of additive noise is incorporated explicitly so as to increase the robustness, thus a complete probabilistic model of the proposed algorithm is built. Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, based on which 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 traditional algorithm and the I1 norm minimization based sparse linear prediction algorithm proposed in recent years. It is shown that the proposed algorithm is more robust to noise and with less distortion, and thus increases the speech quality in applications with ambient noise.

关 键 词：线性预测算法 噪声环境 语音质量 稀疏性 鲁棒性 线性预测分析 概率模型 贝叶斯方法 

分 类 号：TN912.3[电子电信—通信与信息系统]