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
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
Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care.
arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.
By Jiyuan Liu, Liangwei Nathan Zheng, Wei Emma Zhang, Xinpei Wang, Weitong Chen