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Speech Emotion Recognition Using Support Vector Machine
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 20 |
| Year of Publication: 2010 |
| Authors: Yashpalsing Chavhan, M. L. Dhore, Pallavi Yesaware |
10.5120/431-636
|
Yashpalsing Chavhan, M. L. Dhore, Pallavi Yesaware . Speech Emotion Recognition Using Support Vector Machine. International Journal of Computer Applications. 1, 20 ( February 2010), 6-9. DOI=10.5120/431-636
Abstract
Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The Support Vector Machine (SVM) is used as classifier to classify different emotional states such as anger, happiness, sadness, neutral, fear, from Berlin emotional database. The LIBSVM is used for classification of emotions. It gives 93.75% classification accuracy for Gender independent case 94.73% for male and 100% for female speech.
References
- Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., and Taylor, J. G., Emotion recognition in human-computer interaction, IEEE Signal Processing magazine, Vol. 18, No. 1, 32-80, Jan. 2001.
- D. Ververidis, and C. Kotropoulos, Automatic speech classification to five emotional states based on gender information, Proceedings of the EUSIPCO2004 Conference, Austria, 341-344, Sept. 2004.
- Christopher. J. C. Burges, A tutorial on support vector machines for pattern recognition, DataMining and Knowledge Discovery, 2(2):955-974, Kluwer Academic Publishers, Boston, 1998.
- Tristan Fletcher, Support Vector Machines Explained, unpublished.
- Burkhardt, Felix; Paeschke, Astrid; Rolfes, Miriam; Sendlmeier, Walter F.; Weiss, Benjamin A Database of German Emotional Speech. Proceedings of Interspeech, Lissabon, Portugal. 2005.
- Fuhai Li, Jinwen Ma, and Dezhi Huang, MFCC and SVM based recognition of Chinese vowels, Lecture Notes in Artificial Intelligence, vol.3802, 812-819, 2005
- M. D. Skowronski and J. G. Harris, Increased MFCC Filter Bandwidth for Noise-Robust Phoneme Recognition, Proc. ICASSP-02, Florida, May 2002.
- YL. Lin and G. Wei, Speech emotion recognition based on HMM and SVM, proceeding of fourth International conference on Machine Learning and Cybernetics,Guangzhou, 18-21 August 2005.
- Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
- C.W Hsu, C.-C. Chang, C.-J. Lin, A Practical Guide to Support Vector Classification, Technical Report, Department of Comptuer Science & Information Engineering, National Taiwan University, Taiwan.
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