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Speech Emotion Recognition Model Based on CRNN-CTC

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2020 International Conference on Applications and Techniques in Cyber Intelligence (ATCI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1244))

Abstract

CRNN (Convolutional Recurrent Neural Network) deep learning model is currently a typical speech emotion recognition technology. When this model is applied, no matter how long the speech sequence is, it will only be converted into an emotional tag. However, the emotional information in speech samples is generally unevenly distributed between frames, which will cause the recognition performance of the model to deteriorate. For this problem, a speech emotion recognition model based on CRNN-CTC (Convolutional Recurrent Neural Network-Connectionist Temporal Classification) is proposed in this paper. On the basis of CRNN model, the speech samples are divided into emotional frames and non-emotional frames first, and then CTC method is used to make the network model focus on the emotional frames of speech for learning to avoid the problem of poor model performance due to the learning of non-emotional frames. Experimental results show that the model achieves the weighted average recall rate (WAR) of 70.11% and the unweighted average recall rate (UAR) of 69.53%. Compared with CRNN model, the performance of speech emotion recognition is significantly improved.

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Acknowledgements

This work was supported by the Characteristics innovation project of colleges and universities of Guangdong Province (Natural Science, No. 2019KTSCX235).

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Authors and Affiliations

  1. School of Information Science and Technology, South China Business College of Guangdong, University of Foreign Studies, Guangzhou, 510545, China

    Zijiang Zhu, Yi Hu, Junhua Wang & Junshan Li

  2. Human Resources Department, South China Business College of Guangdong University of Foreign Studies, Guangzhou, 510545, China

    Weihuang Dai

Authors
  1. Zijiang Zhu
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  2. Weihuang Dai
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  3. Yi Hu
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  4. Junhua Wang
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  5. Junshan Li
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Corresponding author

Correspondence to Weihuang Dai .

Editor information

Editors and Affiliations

  1. Distributed System and Security Research Cluster, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  4. University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhu, Z., Dai, W., Hu, Y., Wang, J., Li, J. (2021). Speech Emotion Recognition Model Based on CRNN-CTC. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_113

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