arXiv AI
EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation
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arXiv Machine Learning
Aug 31
Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity
arXiv:2511.07011v2 Announce Type: replace-cross Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research t...
arXiv Computation and Language
Aug 21
Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization
arXiv:2501. 16106v2 Announce Type: replace Abstract: Recent advances in multimodal depression recognition for clinical interviews (MDRC) have demonstrated the potential of AI systems by integrating textual, acoustic, and facial cues.
arXiv AI
Sep 3
Interpretable Symptom Vectors for Depression in a Large Language Model
The study investigates how a large language model, Gemma-3-27B-PT, internally represents depressive symptoms. By applying mechanistic interpretability methods to the model’s residual stream, researchers found that symptom groups are geometrically distinct at layer 21, and that projected symptom vectors align with clinician-annotated rankings across mood, somatic, and suicidality dimensions. Additionally, a single depression vector at this layer can differentiate depressive from non-depressive text with an AUC of 0.789, suggesting a potential emotional valence gate for symptom projection.
arXiv Computation and Language
Sep 10
Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health
arXiv:2604.27846v2 Announce Type: replace Abstract: How people narrate their experiences offers a window into how the mind organizes them. Computational approaches to therapeutic writing have evolved...
arXiv AI
Jun 11
MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation
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.
arXiv AI
Jul 29
DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment
arXiv:2607. 25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels.