Home >

news Help

Publication Information


Title
Japanese: 
English:Detection of Depression Using Web-Interview Data by LLM Enhanced with Multimodal Features 
Author
Japanese: Isaac MORALES NOLASCO, 篠田 浩一, Momoko KITAZAWA, Yuriko KAISE, Shunsuke TAKAGI, Genichi SUGIHARA, Taishiro KISHIMOTO.  
English: Isaac MORALES NOLASCO, Koichi SHINODA, Momoko KITAZAWA, Yuriko KAISE, Shunsuke TAKAGI, Genichi SUGIHARA, Taishiro KISHIMOTO.  
Language English 
Journal/Book name
Japanese:電子情報通信学会技術研究報告 
English:IEICE technical report 
Volume, Number, Page Vol. 125    no. 348    pp. 49-54
Published date Jan. 22, 2026 
Publisher
Japanese:一般社団法人電子情報通信学会 
English:The Institute of Electronics, Information and Communication Engineers 
Conference name
Japanese:電子情報通信学会パターン認識・メディア理解研究会 
English: 
Conference site
Japanese:大阪 
English: 
File
Official URL https://ken.ieice.org/ken/paper/20260130TcR6/
 
Abstract Depression is a complex mental disorder that has been widely studied. Using various machine learning techniques, researchers have been able to predict whether an individual is healthy or experiencing depression. The most common approach involves analyzing a person’s voice and speech content. Multimodal approaches improve prediction accuracy by incorporating facial features. With the rising popularity of large language models (LLMs), these models have recently been applied to evaluate symptoms of depression. In this paper, we explore the use of LLMs combined with audio and facial features for binary classification of depression using a Japanese dataset. The current method obtained an average accuracy of 0.7956 using the DSM-5 labeling with a 5-fold cross-validation.

©2007 Institute of Science Tokyo All rights reserved.