Estimating Depressive Symptom Class from Voice

Takeshi Takano (), Daisuke Mizuguchi, Yasuhiro Omiya, Masakazu Higuchi, Mitsuteru Nakamura, Shuji Shinohara, Shunji Mitsuyoshi, Taku Saito, Aihide Yoshino, Hiroyuki Toda and Shinichi Tokuno
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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
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

IJERPH, 2023, vol. 20, issue 5, 1-9

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.

Keywords: voice analysis; major depressive disorder; decision tree (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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