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Emotional-speech recognition using the neuro-fuzzy network
- Authors
- Viswanathan, M.; Zhang, Z.-X.; Tian, X.-W.; Lim, J.S.
- Issue Date
- 2012
- Publisher
- Association for Computing Machinery
- Keywords
- Emotional-speech recognition; Feature selection; Fuzzy classifier; Neuro-fuzzy network
- Citation
- Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12
- Journal Title
- Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12
- ISSN
- 0000-0000
- Abstract
- Emotion recognition based on a speech signal is one of the intensively studied research topics in the domains of human-computer interaction and affective computing. The presented paper is concerned with emotional-speech recognition based on the neuro-fuzzy network with a weighted fuzzy membership function (NEWFM). NEWFM has a feature selection method and makes fuzzy classifiers. In this paper, NEWFM was utilized for classifying four kinds of emotional-speech signals. This NEWFM classification method achieves as high as 86% overall classification accuracy. Significantly, the NEWFM classifier efficiently detects sadness, with a 97.5% recognition rate. © 2012 ACM.
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Related Researcher
- Lim, Joon Shik
- College of IT Convergence (컴퓨터공학부(컴퓨터공학전공))