Speech analysis framework aids mental health clinical decisions

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

影响 This research offers a transparent and interpretable method for using AI to analyze speech patterns, potentially improving objective assessment of mental health conditions.

排序理由 The cluster contains an academic paper detailing a new framework for analyzing speech features for clinical decision support in mental health. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• Androids
• DAIC-WOZ
• SHAP
• StressID
• Vassilis Lyberatos
• XGBoost

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