@inproceedings{47984924222a448485001eaebb8a39db,
title = "An attempt to estimate depressive status from voice",
abstract = "In the whole world especially developed countries, increasing mental health disorders is a serious problem. As a countermeasure, the main objective of this paper is an attempt to estimate depressive status from voice. In this study, we gathered patients with major depressive disorders in the hospital{\textquoteright}s consulting room. Several questionnaires including “the Hamilton Depression Rating Scale” (HAM-D) were administered to evaluate the patients{\textquoteright} depressed state. Voices corresponding to three long vowels were recorded from the subjects. Next, the acoustic feature quantity was calculated based on the voice. We developed the HAM-D score estimation algorithm from the voice using one of three types of long vowel audio content. As a result, there was a correlation between the “Actual HAM-D Score” and the “Estimated HAM-D Score”. We found that the algorithm is effective in estimating depression state and can be used for estimating the disease state based on voice.",
keywords = "Depressive status estimation, The Hamilton Depression Rating Scale (HAM-D), Vocal analysis",
author = "Yasuhiro Omiya and Takeshi Takano and Tomotaka Uraguchi and Mitsuteru Nakamura and Masakazu Higuchi and Shuji Shinohara and Shunji Mitsuyoshi and Mirai So and Shinichi Tokuno",
year = "2019",
doi = "10.1007/978-3-030-25872-6_13",
language = "English (US)",
isbn = "9783030258719",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "168--175",
editor = "Pietro Cipresso and Silvia Serino and Daniela Villani",
booktitle = "Pervasive Computing Paradigms for Mental Health - 9th International Conference, MindCare 2019, Proceedings",
note = "9th International Conference on Pervasive Computing Paradigms for Mental Health, MindCare 2019 ; Conference date: 23-04-2019 Through 24-04-2019",
}