| Item type |
SIG Technical Reports(1) |
| 公開日 |
2024-05-08 |
| タイトル |
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|
タイトル |
Detection of Depression Using Web-Interview Data |
| タイトル |
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言語 |
en |
|
タイトル |
Detection of Depression Using Web-Interview Data |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
セッション3(PRMU) |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
|
資源タイプ |
technical report |
| 著者所属 |
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Shinoda Laboratory, Department of Computer Science, School of Computing, Tokyo Institute of Technology |
| 著者所属 |
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Shinoda Laboratory, Department of Computer Science, School of Computing, Tokyo Institute of Technology |
| 著者所属 |
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Shinoda Laboratory, Department of Computer Science, School of Computing, Tokyo Institute of Technology |
| 著者所属 |
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Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine |
| 著者所属 |
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Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine |
| 著者所属 |
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Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University |
| 著者所属 |
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Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University |
| 著者所属 |
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Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine |
| 著者所属(英) |
|
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|
en |
|
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Shinoda Laboratory, Department of Computer Science, School of Computing, Tokyo Institute of Technology |
| 著者所属(英) |
|
|
|
en |
|
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Shinoda Laboratory, Department of Computer Science, School of Computing, Tokyo Institute of Technology |
| 著者所属(英) |
|
|
|
en |
|
|
Shinoda Laboratory, Department of Computer Science, School of Computing, Tokyo Institute of Technology |
| 著者所属(英) |
|
|
|
en |
|
|
Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine |
| 著者所属(英) |
|
|
|
en |
|
|
Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine |
| 著者所属(英) |
|
|
|
en |
|
|
Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University |
| 著者所属(英) |
|
|
|
en |
|
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Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University |
| 著者所属(英) |
|
|
|
en |
|
|
Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine |
| 著者名 |
Cheuk, Hee Lam
Nathania, Nah
Koichi, Shinoda
Momoko, Kitazawa
Yuriko, Kaise
Shunsuke, Takagi
Genichi, Sugihara
Taishiro, Kishimoto
|
| 著者名(英) |
Cheuk, Hee Lam
Nathania, Nah
Koichi, Shinoda
Momoko, Kitazawa
Yuriko, Kaise
Shunsuke, Takagi
Genichi, Sugihara
Taishiro, Kishimoto
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This paper presents a method for integrating speech, text, and video modalities for multimodal depression detection. Our work leverages shorter utterances to enhance depression detection accuracy, rather than relying on traditional long-term approaches. We introduce the COI-NEXT dataset, comprising authentic clinical interviews conducted through Zoom. Our experiments show that video modalities, particularly when using shorter utterances, lead to improved accuracy for depression detection in patients. Despite limitations due to data scarcity, this work offers valuable insights into multimodal depression detection, emphasizing the significance of multimodal integration in mental health research. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This paper presents a method for integrating speech, text, and video modalities for multimodal depression detection. Our work leverages shorter utterances to enhance depression detection accuracy, rather than relying on traditional long-term approaches. We introduce the COI-NEXT dataset, comprising authentic clinical interviews conducted through Zoom. Our experiments show that video modalities, particularly when using shorter utterances, lead to improved accuracy for depression detection in patients. Despite limitations due to data scarcity, this work offers valuable insights into multimodal depression detection, emphasizing the significance of multimodal integration in mental health research. |
| 書誌レコードID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11131797 |
| 書誌情報 |
研究報告コンピュータビジョンとイメージメディア（CVIM）
巻 2024-CVIM-238,
号 61,
p. 1-5,
発行日 2024-05-08
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| ISSN |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2188-8701 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-CVIM24238061.pdf (1.4 MB)
