| アイテムタイプ |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2023-09-28 |
| 書誌情報 |
en : International Journal of Environmental Research and Public Health
巻 20,
号 2,
p. 1588,
ページ数 15,
発行日 2023-01-15
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| タイトル |
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|
タイトル |
Classification of Depression and Its Severity Based on Multiple Audio Features Using a Graphical Convolutional Neural Network |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
audio feature |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
depression |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
classification model |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
correlation |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
graph convolutional neural network |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
石丸, 桃子
岡田, 吉史
内山, 竜之介
堀口, 凌
豊島, 依槻
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| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Audio features are physical features that reflect single or complex coordinated movements in the vocal organs. Hence, in speech-based automatic depression classification, it is critical to consider the relationship among audio features. Here, we propose a deep learning-based classification model for discriminating depression and its severity using correlation among audio features. This model represents the correlation between audio features as graph structures and learns speech characteristics using a graph convolutional neural network. We conducted classification experiments in which the same subjects were allowed to be included in both the training and test data (Setting 1) and the subjects in the training and test data were completely separated (Setting 2). The results showed that the classification accuracy in Setting 1 significantly outperformed existing state-of-the-art methods, whereas that in Setting 2, which has not been presented in existing studies, was much lower than in Setting 1. We conclude that the proposed model is an effective tool for discriminating recurring patients and their severities, but it is difficult to detect new depressed patients. For practical application of the model, depression-specific speech regions appearing locally rather than the entire speech of depressed patients should be detected and assigned the appropriate class labels. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| DOI |
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|
関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
10.3390/ijerph20021588 |
| PMID |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
PMID |
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関連識別子 |
36674342 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1660-4601 |
| 権利 |
|
|
権利情報 |
© 2023 by the authors. Licensee MDPI |
|
言語 |
en |
| 著者版フラグ |
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|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
ijerph-20-01588-v2.pdf (1.6 MB)
