A multi-stage explainability framework integrates SHAP, theory-informed linguistic features, and a four-stage LLM pipeline to translate speech-based cognitive impairment model outputs into clinically grounded narratives.
The voice of equity: A systematic evaluation of bias mitigation techniques for speech-based cognitive impairment detection across architec- tures and demographics,
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From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection
A multi-stage explainability framework integrates SHAP, theory-informed linguistic features, and a four-stage LLM pipeline to translate speech-based cognitive impairment model outputs into clinically grounded narratives.