@article{ref1,
title="Detectionof major depressive disorder based on a combination of voice features: an exploratory approach",
journal="International journal of environmental research and public health",
year="2022",
author="Higuchi, Masakazu and Nakamura, Mitsuteru and Shinohara, Shuji and Omiya, Yasuhiro and Takano, Takeshi and Mizuguchi, Daisuke and Sonota, Noriaki and Toda, Hiroyuki and Saito, Taku and So, Mirai and Takayama, Eiji and Terashi, Hiroo and Mitsuyoshi, Shunji and Tokuno, Shinichi",
volume="19",
number="18",
pages="e11397-e11397",
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.<p /> <p>Language: en</p>",
language="en",
issn="1661-7827",
doi="10.3390/ijerph191811397",
url="http://dx.doi.org/10.3390/ijerph191811397"
}