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Deep Learning Based Emotion Recognition from Chinese Speech

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Inclusive Smart Cities and Digital Health (ICOST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9677))

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Abstract

Emotion Recognition is challenging for understanding people and enhance human computer interaction experiences. In this paper, we explore deep belief networks (DBN) to classify six emotion status: anger, fear, joy, neutral status, sadness and surprise using different features fusion. Several kinds of speech features such as Mel frequency cepstrum coefficient (MFCC), pitch, formant, et al., were extracted and combined in different ways to reflect the relationship between feature combinations and emotion recognition performance. We adjusted different parameters in DBN to achieve the best performance when solving different emotions. Both gender dependent and gender independent experiments were conducted on the Chinese Academy of Sciences emotional speech database. The highest accuracy was 94.6 %, which was achieved using multi-feature fusion. The experiment results show that DBN based approach has good potential for practical usage of emotion recognition, and suitable multi-feature fusion will improve the performance of speech emotion recognition.

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Notes

  1. 1.

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Acknowledgement

This research was supported by the International S&T Cooperation Program of China (ISTCP, 2013DFA10980).

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Authors and Affiliations

  1. Department of Software Engineering, China University of Petroleum, No.66 Changjiang West Road, Qingdao, 266580, China

    Weishan Zhang, Dehai Zhao & Yuanjie Zhang

  2. Hisense TransTech Co., Ltd., No.16 Shandong Road, Qingdao, China

    **ufeng Chen

Authors
  1. Weishan Zhang
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  2. Dehai Zhao
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  3. **ufeng Chen
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  4. Yuanjie Zhang
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Corresponding author

Correspondence to Weishan Zhang .

Editor information

Editors and Affiliations

  1. Iowa State University, Ames, Iowa, USA

    Carl K. Chang

  2. University of Bologna, Bologna, Italy

    Lorenzo Chiari

  3. The University of Massachusetts, Lowell, Massachusetts, USA

    Yu Cao

  4. Huazhong Univ. of Science and Technology, Wuhan, China

    Hai **

  5. Institut Mines Télécom Paris/CNRS, Paris, France

    Mounir Mokhtari

  6. Institut Mines Télécom, Paris, France

    Hamdi Aloulou

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Zhang, W., Zhao, D., Chen, X., Zhang, Y. (2016). Deep Learning Based Emotion Recognition from Chinese Speech. In: Chang, C., Chiari, L., Cao, Y., **, H., Mokhtari, M., Aloulou, H. (eds) Inclusive Smart Cities and Digital Health. ICOST 2016. Lecture Notes in Computer Science(), vol 9677. Springer, Cham. https://doi.org/10.1007/978-3-319-39601-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-39601-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39600-2

  • Online ISBN: 978-3-319-39601-9

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