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.
Improving depression prediction ac- curacy using fisher score-based feature selection and dynamic ensemble selection approach based on acoustic features of speech,
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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.