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International Conference on Advanced Intelligent Systems and Informatics

AISI 2016: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 pp 85–95Cite as

Optimizing Fuzzy Inference Systems for Improving Speech Emotion Recognition

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

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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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan

    Reda Elbarougy & Masato Akagi

  2. Department of Mathematics, Faculty of Science, Damietta University, Damietta, Egypt

    Reda Elbarougy

Authors
  1. Reda Elbarougy
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  2. Masato Akagi
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Corresponding author

Correspondence to Reda Elbarougy .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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