NDLI: Speech emotion recognition using combination of features

Content Provider	IEEE Xplore Digital Library
Author	Qingli Zhang Ning An Kunxia Wang Fuji Ren Lian Li
Copyright Year	2013
Description	Author affiliation: Univ. of Tokushima, Tokushima, Japan (Fuji Ren) \|\| Hefei Univ. of Technol., Hefei, China (Ning An; Kunxia Wang; Lian Li) \|\| Lanzhou Univ., Lanzhou, China (Qingli Zhang)
Abstract	In this paper, we study how speech features' numbers and statistical values impact recognition accuracy of emotions present in speech. With Gaussian Mixture Model (GMM), we identify two effective features, namely Mel Frequency Cepstrum Coefficients (MFCCs) and Auto Correlation Function Coefficients (ACFC) extracted directly from speech signal. Using GMM supervector formed by values of MFCCs, delta MFCCs and ACFC, we conduct experiments with Berlin emotional database considering six previously proposed emotions: anger, disgust, fear, happy, neutral and sad. Our method achieve emotion recognition rate of 74.45%, significantly better than 59.00% achieved previously. To prove the broad applicability of our method, we also conduct experiments considering a different set of emotions: anger, boredom, fear, happy, neutral and sad. Our emotion recognition rate of 75.00% is again better than71.00% of the method of hidden Markov model with MFCC, delta MFCC, cepstral coefficient and speech energy.
Starting Page	523
Ending Page	528
File Size	714703
Page Count	6
File Format	PDF
ISBN	9781467362481
e-ISBN	9781467362498
DOI	10.1109/ICICIP.2013.6568131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Accuracy Correlation Speech recognition Speech Feature extraction Mel frequency cepstral coefficient
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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