Automated assessment of depression from facial dynamics holds promise for scalable mental health monitoring, yet cross-corpus generalization of learned biomarkers remains an open challenge. We present a systematic bidirectional transfer study pairing the EmpkinS-EKSpression randomized controlled trial (RCT; N = 256, SCID-5-CV diagnoses) with the Extended Distress Analysis Interview Corpus (E-DAIC; N = 275, semi-structured clinical interviews), predicting depression severity and binary diagnostic status from facial action units, head pose, and gaze. Cross-corpus binary classification proves more robust than continuous PHQ-8 severity regression, with forward transfer achieving AUC = 0.70. Regression transfer is governed by functional context alignment: passive observation phases yield the most transferable models, while active emotion regulation phases elicit stronger within-corpus signals. These findings establish functional context alignment as the primary determinant of cross-corpus generalization, with passive elicitation contexts offering the best trade-off between within-corpus sensitivity and cross-corpus robustness.
BACKGROUND
Major depressive disorder exists along a continuum, from health through remission to active depression. Differentiating these states remains challenging, and suicide risk may not be fully captured by self-report. Objective markers, such as speech, which integrates affective, cognitive, and motor processes, o...
Qun-Xing Lin, Xiao-Hua Wu, Shan Huang et al.· Journal of Affective Disorde...· 0 citations
Self-report scales dominate adolescent mental-health assessment but depend on symptom recognition and disclosure. Participant-generated language offers a complementary window on affective processes, yet most evidence comes from adult, English-language, community, or social-media data, leaving clinically referred adoles...
Z. S. Albayrak, Herdem Aslan Genç, Ezgi Eymen Ekici et al.· Frontiers in Psychology· 0 citations
The interactional design of digital mental health systems can shape what people say, how they say it, and whose distress becomes legible to automated models. We developed a multimodal survey chatbot, M2SurveyBot with clinical psychologists, to examine whether interaction modality that is, typing versus speech, affects...
Sunny Rai, N. K. R. Sehgal, K. Nguyen et al.· Proceedings of the 2026 Comp...· 0 citations
This framework successfully decodes cognitive coping strategies independent of depressive mood, providing potentially actionable targets for personalized psychiatric interventions and a promising, interpretable digital biomarker for psychological resilience.
Shu-Yao Wang, Xue-Quan Zhu, Nan-Xi Li et al.· BMC Psychiatry· 0 citations
MIDAS is presented, an interview-aware multimodal framework that preserves the question-response structure of clinical interviews for depression assessment and integrates linguistic, acoustic, and visual-behavioral representations through gated multimodal fusion and shared large language model-based contextual modeling...
A decorrelated, clinician-aligned symptom signal readable directly from internal activations is revealed, offering a mechanistic foundation for interpretable depression-assessment tools.
Fang-Ying Zhu, Ajay K. Subramanian, A. Constant et al.· 1 citation
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
