Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
arXiv:2605.24678v1 Announce Type: cross 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, a
Advances in AI, particularly in natural language processing and machine learning, coupled with increasing demand for accessible mental health solutions, make robust clinical decision-support systems using speech features feasible now.
This development highlights the growing capability of AI to provide objective, interpretable insights in sensitive domains like mental health, potentially democratizing access to assessment and improving diagnostic accuracy.
The explicit focus on 'perceptual' speech features and 'interpretable machine learning' suggests a move towards AI systems that are more transparent and clinically validated, shifting from 'black box' approaches to more actionable insights for healthcare professionals.
- · Mental Health Tech Startups
- · Healthcare AI Developers
- · Patients with Mental Health Conditions
- · Clinical Researchers
- · Traditional Diagnostic Providers (if not adopting AI)
- · Black-box AI Solutions
- · Legacy Mental Health Assessment Tools
AI-powered tools become a standard component in initial mental health assessments, offering prescreening and early detection capabilities.
Increased demand for ethically robust and explainable AI in healthcare, driving regulatory frameworks and investment in 'interpretable AI' research.
The application of similar speech analysis frameworks expands to other medical fields requiring subtle diagnostic cues, further integrating AI into mainstream clinical practice.
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Read at arXiv cs.CL