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Speech Emotion Recognition Using Local and Global Features

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Brain Informatics (BI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

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Abstract

Speech is an easy and useful way to detect speakers’ mental and psychological health, and automatic emotion recognition in speech has been investigated widely in the fields of human-machine interaction, psychology, psychiatry, etc. In this paper, we extract prosodic and spectral features including pitch, MFCC, intensity, ZCR and LSP to establish the emotion recognition model with SVM classifier. In particular, we find different frame duration and overlap have different influences on final results. So, Depth-First-Search method is applied to find the best parameters. Experimental results on two known databases, EMODB and RAVDESS, show that this model works well, and our speech features are enough effectively in characterizing and recognizing emotions.

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Acknowledgments

The research was supported in part by NSFC under Grants 11301504 and U1536104, in part by National Basic Research Program of China (973 Program2014CB744600).

Author information

Authors and Affiliations

  1. School of Computer and Control, University of Chinese Academy of Sciences, Beijing, 100190, China

    Yuanbo Gao & Baobin Li

  2. Beijing Institue of Electronics Technology and Application, Beijing, 100091, China

    Ning Wang

  3. Institute of Psychology Chinese Academy of Sciences, Beijing, 100101, China

    Tingshao Zhu

Authors
  1. Yuanbo Gao
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  2. Baobin Li
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  3. Ning Wang
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  4. Tingshao Zhu
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Corresponding author

Correspondence to Baobin Li .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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Gao, Y., Li, B., Wang, N., Zhu, T. (2017). Speech Emotion Recognition Using Local and Global Features. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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