Framework applies XAI feature selection to low-complexity ML models for interpretable, fair speech-based depression detection on DAIC-WOZ, claiming 82% accuracy as state-of-the-art.
Common pitfalls and recommendations for use of machine learning in depression severity estimation: Daic-woz study,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.AS 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Fair and Transparent Framework for Speech-Based Depression Detection: Balancing Interpretability and Performance
Framework applies XAI feature selection to low-complexity ML models for interpretable, fair speech-based depression detection on DAIC-WOZ, claiming 82% accuracy as state-of-the-art.