From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection
The framework translates complex speech recognition model predictions into understandable clinical narratives for cognitive impairment detection. It combines SHAP attribution, linguistic features, and a four-stage LLM pipeline to map model outputs onto cognitive dimensions like lexical richness. This bridges the gap between black-box AI and actionable physician insights.