Abstract
Fuzzy Inference System (FIS) is used for pattern recognition and classification purposes in many fields such as emotion recognition. However, the performance of FIS is highly dependent on the radius of clusters which has a very important role for its recognition accuracy. Although many researcher initialize this parameter randomly which does not grantee the best performance of their systems. The purpose of this paper is to optimize FIS parameters in order to construct a high efficient system for speech emotion recognition. Therefore, an optimization algorithm based on particle swarm optimization technique is proposed for finding the best parameters of FIS classifier. In order to evaluate the proposed system it was tested using two emotional speech databases; Fujitsu and Berlin database. The simulation results show that the optimized system has high recognition accuracy for both languages with 97 % recognition accuracy for Japanese and 80 % for German database.
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Acknowledgments
This study was supported by the Grant-in-Aid for Scientific Research (A) (No. 25240026).
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Elbarougy, R., Akagi, M. (2017). Optimizing Fuzzy Inference Systems for Improving Speech Emotion Recognition. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_9
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DOI: https://doi.org/10.1007/978-3-319-48308-5_9
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