| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2023-10-05 |
| 書誌情報 |
en : Diagnostics
巻 13,
号 4,
p. 727,
ページ数 8,
発行日 2023-02-14
|
| タイトル |
|
|
タイトル |
A New Regression Model for Depression Severity Prediction Based on Correlation among Audio Features Using a Graph 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 |
regression 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 |
|
資源タイプ |
journal article |
| 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 |
Recent studies have revealed mutually correlated audio features in the voices of depressed patients. Thus, the voices of these patients can be characterized based on the combinatorial relationships among the audio features. To date, many deep learning–based methods have been proposed to predict the depression severity using audio data. However, existing methods have assumed that the individual audio features are independent. Hence, in this paper, we propose a new deep learning–based regression model that allows for the prediction of depression severity on the basis of the correlation among audio features. The proposed model was developed using a graph convolutional neural network. This model trains the voice characteristics using graph-structured data generated to express the correlation among audio features. We conducted prediction experiments on depression severity using the DAIC-WOZ dataset employed in several previous studies. The experimental results showed that the proposed model achieved a root mean square error (RMSE) of 2.15, a mean absolute error (MAE) of 1.25, and a symmetric mean absolute percentage error of 50.96%. Notably, RMSE and MAE significantly outperformed the existing state-of-the-art prediction methods. From these results, we conclude that the proposed model can be a promising tool for depression diagnosis. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| DOI |
|
|
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/diagnostics13040727 |
| 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 |
36832211 |
| 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 |
2075-4418 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. |
| 著者版フラグ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
diagnostics-13-00727-v2.pdf (1.1 MB)
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