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.
Common Pitfalls and Recommendations for Use of Machine Learning in Depression Severity Estimation: DAIC-WOZ Study,
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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.