Estimating depressive status from voice

Yasuhiro Omiya, Takeshi Takano, Tomotaka Uraguchi, Masakazu Higuchi, Shuji Shinohara, Mitsuteru Nakamura, Shunji Mitsuyoshi, Mirai So, Shinichi Tokuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We developed an algorithm to estimate the depression status from a person's voice signal. In the experiment, we collected voice samples from patients with major depression. In addition, questionnaires concerning the patients' depressed mood were obtained. The voice signals were collected for the subjects' vocalizations of three types of long vowels. Next, acoustic features were calculated based on the speech. Subsequently, an algorithm was developed to estimate the severity of depression, judged by the HAM-D score, from the recorded voice samples. The results indicated that the algorithm performed well at estimating the severity of the HAM-D score using the acoustic features of the long vowels. Consequently, the algorithm also performed well at estimating the depressed mood, thus suggesting the utility of the algorithm for estimating depression conditions based on speech.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2795-2796
Number of pages2
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Externally publishedYes
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

Keywords

  • Depressive status estimation
  • HAM-D
  • Mental health state estimation
  • Voice

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Omiya, Y., Takano, T., Uraguchi, T., Higuchi, M., Shinohara, S., Nakamura, M., Mitsuyoshi, S., So, M., & Tokuno, S. (2019). Estimating depressive status from voice. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2795-2796). [8621326] (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621326