A robust feature extraction approach based on an auditory model for classification of speech and expressiveness  

作　　者：孙颖 V.Werner 张雪英 

机构地区：[1]College of Information Engineering,Taiyuan University of Technology [2]Department Electronic and Informatics,Laboratory for Digital Speech and Audio Processing,Vrije Universiteit Brussel

出　　处：《Journal of Central South University》2012年第2期504-510,共7页中南大学学报（英文版）

基　　金：Project(61072087)supported by the National Natural Science Foundation of China;Project(2010011020-1)supported by the Natural Scientific Foundation of Shanxi Province,China;Project(20093010)supported by Graduate Innovation Fundation of Shanxi Province,China

摘　　要：Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.Based on an auditory model, the zero-crossings with maximal Teager energy operator （ZCMT） feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral （non-emotional） speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator （TEO）. Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology （TYUT） and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.

关 键 词：speech recognition emotion recognition zero-crossings Teager energy operator speech database 

分 类 号：TN912.3[电子电信—通信与信息系统]