Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach

Masakazu Higuchi, Mitsuteru Nakamura, Shuji Shinohara, Yasuhiro Omiya, Takeshi Takano, Daisuke Mizuguchi, Noriaki Sonota, Hiroyuki Toda, Taku Saito, Mirai So, Eiji Takayama, Hiroo Terashi, Shunji Mitsuyoshi and Shinichi Tokuno
Additional contact information
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
Yasuhiro Omiya: PST Inc., Yokohama 231-0023, Japan
Takeshi Takano: PST Inc., Yokohama 231-0023, Japan
Daisuke Mizuguchi: PST Inc., Yokohama 231-0023, Japan
Noriaki Sonota: Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
Hiroyuki Toda: Department of Psychiatry, School of Medicine, National Defense Medical College, Saitama 359-8513, Japan
Taku Saito: Department of Psychiatry, School of Medicine, National Defense Medical College, Saitama 359-8513, Japan
Mirai So: Department of Neuropsychiatry, Tokyo Dental College, Tokyo 101-0061, Japan
Eiji Takayama: Department of Oral Biochemistry, Asahi University School of Dentistry, Gifu 501-0296, Japan
Hiroo Terashi: Department of Neurology, Tokyo Medical University, Tokyo 160-8402, Japan
Shunji Mitsuyoshi: Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
Shinichi Tokuno: Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan

IJERPH, 2022, vol. 19, issue 18, 1-13

Abstract: In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Keywords: voice analysis; major depressive disorder; logistic regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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