Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Lam Cheuk Hee, Cheuk Hee Lam, Nah Nathania, Nathania Nah, 篠田 浩一, Koichi Shinoda, 北沢 桃子, Momoko Kitazawa, 貝瀬 有里子, Yuriko Kaise, 高木 俊輔, Shunsuke Takagi, 杉原 玄一, Genichi Sugihara, 岸本 泰士郎, Taishiro Kishimoto
Institution: Tokyo Medical and Dental University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6995330452
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