A Model-Based Soft Decision Approach for Speech Enhancement  

作　　者：Xianyun Wang Changchun Bao Feng Bao 

机构地区：[1]Speech and Audio Signal Processing Laboratory,Faculty of Information Technology,Beijing University of Technology [2]Department of Electrical and Computer Engineering,The University of Auckland

出　　处：《China Communications》2017年第9期11-22,共12页中国通信（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.61471014,61231015)

摘　　要：Many speech enhancement algorithms that deal with noise reduction are based on a binary masking decision(termed as the hard decision), which may cause some regions of the synthesized speech to be discarded. In view of the problem, a soft decision is often used as an optimal technique for speech restoration. In this paper, considering a new fashion of speech and noise models, we present two model-based soft decision techniques. One technique estimates a ratio mask generated by the exact Bayesian estimators of speech and noise. For the second technique, we consider one issue that an optimum local criterion(LC) for a certain SNR may not be appropriate for other SNRs. So we estimate a probabilistic mask with a variable LC. Experimental results show that the proposed method achieves a better performance than reference methods in speech quality.Many speech enhancement algorithms that deal with noise reduction are based on a binary masking decision(termed as the hard decision), which may cause some regions of the synthesized speech to be discarded. In view of the problem, a soft decision is often used as an optimal technique for speech restoration. In this paper, considering a new fashion of speech and noise models, we present two model-based soft decision techniques. One technique estimates a ratio mask generated by the exact Bayesian estimators of speech and noise. For the second technique, we consider one issue that an optimum local criterion(LC) for a certain SNR may not be appropriate for other SNRs. So we estimate a probabilistic mask with a variable LC. Experimental results show that the proposed method achieves a better performance than reference methods in speech quality.

关 键 词：SPEECH ENHANCEMENT SOFT masks CASA THRESHOLD 

分 类 号：TN912.35[电子电信—通信与信息系统]