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An Explained Artificial Intelligence-Based Solution to Identify Depression Severity Symptoms Using Acoustic Features

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Abstract

This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that k-nearest neighbor and random forest form the group having the most acceptable results. Regarding our second objective, we determined the importance of the features of our best model using the SHapley Additive exPlanations approach: our findings showed that the fourth Mel-frequency cepstral coefficients, harmonic difference, and shimmer are the most important features.

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Funding

This study was supported by the grant for research centers in the field of AI provided by the Analytical Center for the Government of the Russian Federation (ACRF) in accordance with the agreement on the provision of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement with HSE University no. 70-2021-00139.

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Authors and Affiliations

  1. Center for Language and Brain, HSE University, Moscow, Russia

    S. Shalileh, M. V. Khudyakova & O. V. Dragoy

  2. Vision Modeling Laboratory, HSE University, Moscow, Russia

    S. Shalileh & A. O. Koptseva

  3. Department of Endogenous Mental Disorders and Affective States, Federal State Budgetary Scientific Institution Mental Health Research Center, Moscow, Russia

    T. I. Shishkovskaya

  4. Center for Language and Brain, HSE University, Nizhny Novgorod, Russia

    M. V. Khudyakova

  5. Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia

    O. V. Dragoy

Authors
  1. S. Shalileh
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  2. A. O. Koptseva
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  3. T. I. Shishkovskaya
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  4. M. V. Khudyakova
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  5. O. V. Dragoy
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Contributions

Sh. S.: Conceptualization, Methodology, Investigation, Software, Validation, Formal analysis, Writing – original draft (WOD), Writing – review and editing. K. A.: Investigation, Software, Validation, WOD. Sh. T.: Data curation. Kh. M.: Conceptualization, Data curation, Writing – review and editing. D. O.: Conceptualization, Writing – review and editing, Resources, Project administration.

All authors read and approved the final manuscript.

Corresponding authors

Correspondence to S. Shalileh, A. O. Koptseva, T. I. Shishkovskaya, M. V. Khudyakova or O. V. Dragoy.

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The authors of this work declare that they have no conflicts of interest.

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Shalileh, S., Koptseva, A.O., Shishkovskaya, T.I. et al. An Explained Artificial Intelligence-Based Solution to Identify Depression Severity Symptoms Using Acoustic Features. Dokl. Math. 108 (Suppl 2), S374–S381 (2023). https://doi.org/10.1134/S1064562423701090

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  • DOI: https://doi.org/10.1134/S1064562423701090

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