计算机应用研究2025，Vol.42Issue(1)：236-241,6.DOI:10.19734/j.issn.1001-3695.2024.05.0206
基于跨模态特征重构与解耦网络的多模态抑郁症检测方法
Multi-modal depression detection method based on cross-modal feature reconstruction and decoupling network
摘要
Abstract
Depression is a widespread and severe mental health disorder,and requires early detection for effective interven-tion.Automated depression detection that integrates audio and text modalities addresses the challenges posed by information re-dundancy and modality heterogeneity.Previous studies often fail to capture the interaction between audio and text modalities for effective depression detection.To overcome these limitations,this study proposed a multi-modal depression detection method based on cross-modal feature reconstruction and a decoupling network(CFRDN).The method used text as the core modality,guiding the model to reconstruct audio features for cross-modal feature decoupling tasks.The framework separated shared and private features from the text-guided reconstructed audio features for subsequent multimodal fusion.Extensive experiments on the DAIC-WoZ and E-DAIC datasets demonstrate that the proposed method outperforms state-of-the-art approaches in multimo-dal depression detection tasks.关键词
多模态/抑郁症检测/特征重构/特征解耦/特征融合Key words
multimodal/depression detection/feature reconstruction/feature decoupling/feature fusion分类
信息技术与安全科学引用本文复制引用
赵小明,谌自强,张石清..基于跨模态特征重构与解耦网络的多模态抑郁症检测方法[J].计算机应用研究,2025,42(1):236-241,6.基金项目
国家自然科学基金资助项目(62276180) （62276180）
