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

Content Provider	Springer Nature Link
Author	Sun, Ying Werner, V. Zhang, Xue ying
Copyright Year	2012
Abstract	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.
Starting Page	504
Ending Page	510
Page Count	7
File Format	PDF
ISSN	20952899
Journal	Journal of Central South University of Technology
Volume Number	19
Issue Number	2
e-ISSN	22275223
Language	English
Publisher	Central South University
Publisher Date	2012-01-28
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	speech recognition emotion recognition zero-crossings Teager energy operator speech database Metallic Materials Engineering
Content Type	Text
Resource Type	Article
Subject	Metals and Alloys Engineering

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