Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
Researchers have developed a framework for analyzing speech features to aid in clinical decision-making for mental health care. This system uses perceptually grounded acoustic and linguistic characteristics, such as prosody, vocal quality, and semantic coherence, to identify objective cues related to depression, anxiety, and ADHD. By employing interpretable machine learning techniques like XGBoost with SHAP and LIME, the framework establishes stable associations between symptom severity and vocal irregularities, lexical-syntactic patterns, and affective tone, validated across both benchmark and clinical datasets. AI
IMPACT This research offers a transparent and interpretable method for using AI to analyze speech patterns, potentially improving objective assessment of mental health conditions.