Estimating depressive status from voice

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

doi:10.1109/BIBM.2018.8621326

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 proceeding › Conference contribution

3 Scopus citations

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 language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2795-2796
Number of pages	2
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621326
State	Published - Jan 21 2019
Externally published	Yes
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

Keywords

Depressive status estimation
HAM-D
Mental health state estimation
Voice

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2018.8621326

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

Estimating depressive status from voice. / Omiya, Yasuhiro; Takano, Takeshi; Uraguchi, Tomotaka et al.

Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2795-2796 8621326 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 8621326, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2795-2796, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621326

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

Omiya, Yasuhiro ; Takano, Takeshi ; Uraguchi, Tomotaka et al. / Estimating depressive status from voice. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2795-2796 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Estimating depressive status from voice",

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.",

keywords = "Depressive status estimation, HAM-D, Mental health state estimation, Voice",

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

note = "Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

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doi = "10.1109/BIBM.2018.8621326",

language = "English (US)",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

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AU - Omiya, Yasuhiro

AU - Takano, Takeshi

AU - Uraguchi, Tomotaka

AU - Higuchi, Masakazu

AU - Shinohara, Shuji

AU - Nakamura, Mitsuteru

AU - Mitsuyoshi, Shunji

AU - So, Mirai

AU - Tokuno, Shinichi

PY - 2019/1/21

Y1 - 2019/1/21

N2 - 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.

AB - 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.

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Y2 - 3 December 2018 through 6 December 2018

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Estimating depressive status from voice

Abstract

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Keywords

ASJC Scopus subject areas

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