arXiv:2607. 05901v1 Announce Type: new Abstract: Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and establishing robust decision boundaries.
By Manning Gao, Tingyi Liu, Leheng Zhang, Haifeng Hu, Yuncheng Jiang, Sijie Mai
arXiv:2607. 22794v1 Announce Type: cross Abstract: Automatic depression detection with deep learning has shown promise but often suffers from limited generalization due to domain shift arising from inter-speaker variability.
By Ali Tabaraei, Federico Simonetta, Stavros Ntalampiras
The paper introduces a multimodal emotion recognition framework that combines audio and visual feature extraction with an attention-based fusion strategy. Audio features include Wav2Vec2 embeddings, MFCCs, and statistical acoustic descriptors, fused via a BiLSTM, while video features are extracted using a ResNet50-BiLSTM architecture. A multi-head attention mechanism fuses these modalities, and experiments on MELD and IEMOCAP show significant accuracy and robustness gains, especially in unbalanced data settings.
By Xu Lin, Ke Wang, Hui Kang, Xinying Wang
arXiv:2607. 03744v1 Announce Type: new Abstract: Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech.
By Hanie Kang, Huang-Cheng Chou, Sudarsana Reddy Kadiri, Shrikanth Narayanan
arXiv:2606. 11197v1 Announce Type: cross Abstract: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-constrained mental health settings.
By Xuzhi Wang, Xinran Wu, Ziping Zhao, Jianhua Tao, Bj\"orn W. Schuller
MERID is a framework that uses recursive self‑improvement agents to autonomously develop multimodal pipelines for detecting major depressive disorder. It aligns multimodal records with depression targets, jointly modifies representations, fusion, and predictors, and guides revisions through evidence‑guided evolution to validate improvements before inheritance. Experiments on depression benchmarks show MERID outperforms existing multimodal and agent‑based baselines, especially highlighting the importance of acoustic and linguistic cues.
By Lei Liu, Zhaokang Liang, Qingcheng Zeng, Chenda Duan, Lu Mi, Zhen Tan, Tianyu Liu