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Detecting Depression in Speech Under Different Speaking Styles and Emotional Valences

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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

Detecting depression in speech is a hot topic in recent years. Some inconsistent results in previous researches imply a few important influence factors are ignored. In this paper, we investigated a sample of 184 subjects (108 females, 76 males) to examine the influence of speaking style and emotional valence on depression detection. First, classification accuracy was used to measure the influence of these two factors. Then, two-way analysis of variance was employed to determine interactive acoustical features. Finally, normalized features by subtracting got higher classification accuracies. Results show that both speaking style and emotional valence are important factors. Spontaneous speech is better than automatic speech and neutral is the best choice among three emotional valences in depression detection. Normalized features improve the detection performance.

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Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744600), the National Natural Science Foundation of China (Grant No. 61632014, No. 61210010), Program of Beijing Municipal Science & Technology Commission (No. Z171100000117005), the Program of International S&T Cooperation of MOST (No. 2013DFA11140). Grateful acknowledgement is made to: Xiang Gao, Tianyang Wang, Lihua Yan, Huanyu Kang, for experimental implementation.

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Authors and Affiliations

  1. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Zhenyu Liu, Bin Hu, Xiaoyu Li & Fei Liu

  2. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Bin Hu

  3. Beijing Anding Hospital of Capital Medical University, Beijing, China

    Gang Wang

  4. Lanzhou University Second Hospital, Lanzhou, China

    Jing Yang

Authors
  1. Zhenyu Liu
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  2. Bin Hu
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  3. Xiaoyu Li
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  4. Fei Liu
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  5. Gang Wang
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  6. Jing Yang
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Corresponding author

Correspondence to Bin Hu .

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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Liu, Z., Hu, B., Li, X., Liu, F., Wang, G., Yang, J. (2017). Detecting Depression in Speech Under Different Speaking Styles and Emotional Valences. 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_25

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_25

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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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