Audit of depression detection benchmarks finds that official splits yield unstable model rankings, zero-shot transfer across datasets is weak, and text models but not audio models improve on symptom-dense interview segments.
Detecting depression using vocal, facial and semantic communication cues,
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A GRU encoder using static acoustic, dynamic, and interaction features from 53 dyads predicts cognitive load related to time pressure, mental work, effort, and performance, with turn-taking linked to temporal demand and imbalanced participation to mental demand.
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A Multi-Probe Audit of Clinical-Interview Depression Detection Benchmarks
Audit of depression detection benchmarks finds that official splits yield unstable model rankings, zero-shot transfer across datasets is weak, and text models but not audio models improve on symptom-dense interview segments.
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Predicting Cognitive Load from Speech and Interaction Dynamics in Dyadic Conversations
A GRU encoder using static acoustic, dynamic, and interaction features from 53 dyads predicts cognitive load related to time pressure, mental work, effort, and performance, with turn-taking linked to temporal demand and imbalanced participation to mental demand.