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Estimating Depressive Symptom Class from Voice

Author

Listed:
  • Takeshi Takano

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Daisuke Mizuguchi

    (PST Inc., Yokohama 231-0023, Japan)

  • Yasuhiro Omiya

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
    PST Inc., Yokohama 231-0023, Japan)

  • Masakazu Higuchi

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Mitsuteru Nakamura

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Shuji Shinohara

    (School of Science and Engineering, Tokyo Denki University, Saitama 350-0394, Japan)

  • Shunji Mitsuyoshi

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan)

  • Taku Saito

    (Department of Psychiatry, National Defense Medical College, Tokorozawa 359-8513, Japan)

  • Aihide Yoshino

    (Department of Psychiatry, National Defense Medical College, Tokorozawa 359-8513, Japan)

  • Hiroyuki Toda

    (Department of Psychiatry, National Defense Medical College, Tokorozawa 359-8513, Japan)

  • Shinichi Tokuno

    (Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
    Graduate School of Health Innovation, Kanagawa University of Human Services, Kawasaki 210-0821, Japan)

Abstract

Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients’ distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression.

Suggested Citation

  • Takeshi Takano & Daisuke Mizuguchi & Yasuhiro Omiya & Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Shunji Mitsuyoshi & Taku Saito & Aihide Yoshino & Hiroyuki Toda & Shinichi Tokuno, 2023. "Estimating Depressive Symptom Class from Voice," IJERPH, MDPI, vol. 20(5), pages 1-9, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:3965-:d:1077717
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