Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care

2026-05-23

AI Summary

This study addresses the lack of objective and interpretable vocal biomarkers in mental health assessment by proposing a transparent, clinically interpretable speech analysis framework. It systematically integrates multidimensional perceptual features—including prosody, voice quality, semantic coherence, syntactic structure, and sarcasm—by jointly leveraging acoustic and linguistic information. Using an XGBoost model enhanced with SHAP and LIME for interpretability, the framework identifies key features such as jitter, shimmer, lexical-syntactic patterns, and affective intonation from real-world clinical data and multiple benchmark datasets. Experimental results demonstrate robust associations between vocal irregularities and symptom severity in depression, anxiety, and ADHD. Ablation studies further confirm the most discriminative feature subsets, offering reliable and explainable vocal biomarkers to support clinical evaluation.

Abstract

Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We present a systematic feature-based analysis framework leveraging perceptually grounded acoustic and linguistic characteristics, including prosody, vocal quality, semantic coherence, syntactic structure, and sarcasm. Using statistical analysis and interpretable machine learning (XGBoost with SHAP and LIME), we examine associations between speech features and validated symptom measures of depression, anxiety, and ADHD. Evaluated on both controlled benchmark datasets (StressID, DAIC-WOZ, Androids, EATD) and a real-world clinical dataset, the framework reveals stable and consistent relationships between symptom severity and vocal irregularities (e.g., shimmer, jitter), lexical-syntactic patterns, and affective tone. An ablation study conducted across all datasets further identifies the most informative feature groups. This work explores a transparent and clinically interpretable approach to speech-based mental health analysis.

Problem

• perceptual speech features
• mental health assessment
• clinical decision-support
• symptom severity
• speech-based analysis

Innovation

• perceptual speech features
• interpretable machine learning
• mental health assessment
• vocal irregularities
• feature ablation study