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A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

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Abstract

Early intervention for depression could provide a means to reducing the disease burden, but there is a lack of objective diagnostic methods. This study investigated automatic depression classification on a speech dataset of 85 healthy controls (51 females and 34 males) and 85 depressed patients (53 females and 32 males). Considering that there are obvious differences in the performance of different types of speech features, we propose a radius-incorporated localized multiple kernel learning (trLMKL) algorithm for detecting depression in speech to make the best use of speech features. To improve the classification accuracy, we combine the information of both the margin and the radius of the MEB to learn the gating model parameters in our algorithm. Furthermore, we do not directly incorporate the radius of the MEB, but incorporate the trace of the total scattering matrix of training data. This method can avoid the time cost of calculating the radius at each iteration and decrease the computational complexity. Comprehensive experiments were carried out on our depressed speech dataset and 10 UCI datasets. Our algorithm achieved better classification performance overall than SimpleMKL and LMKL, and it was efficient at detecting depression, indicating its potential for use as a diagnostic method for depression.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This work was supported by the National Basic Research Program of China (973 Program) (No.2014CB744600).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China

    Haihua Jiang

  2. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Haihua Jiang & Bin Hu

  3. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Bin Hu & Zhenyu Liu

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

    Gang Wang

  5. Lanzhou University Second Hospital, Lanzhou, China

    Lan Zhang

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  1. Haihua Jiang
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  2. Bin Hu
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  3. Zhenyu Liu
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  4. Gang Wang
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  5. Lan Zhang
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Correspondence to Haihua Jiang or Bin Hu.

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Jiang, H., Hu, B., Liu, Z. et al. A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech. Int J Speech Technol 26, 371–378 (2023). https://doi.org/10.1007/s10772-023-10017-0

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  • DOI: https://doi.org/10.1007/s10772-023-10017-0

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