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AI Research
arXiv CS.AI·May 26·5 min read
Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
TL;DR
Mental health assessments often lack objective measures. A framework was developed using perceptual speech features, such as vocal quality and prosody, analyzed with interpretable machine learning techniques like XGBoost.
✦ Why It Matters
Engineers can leverage this framework to develop tools for objective mental health assessments using speech analysis.
Key Takeaways
- Explore using speech features for mental health diagnostics.
- Implement interpretable machine learning techniques like XGBoost.
- Focus on vocal irregularities and linguistic patterns.
- Utilize findings to enhance clinical decision-support systems.
Full Summary
Mental health care often relies on subjective assessments, which can be inconsistent. To address this, a systematic analysis framework was created that leverages perceptual speech features, including prosody (the rhythm and intonation of speech), vocal quality, and linguistic characteristics like semantic coherence and sarcasm.
The researchers employed interpretable machine learning techniques, specifically XGBoost combined with SHAP and LIME, to analyze the relationship between these speech features and validated symptom measures for depression, anxiety, and ADHD. Evaluations were conducted on both controlled benchmark datasets and a real-world clinical dataset, revealing stable associations between symptom severity and vocal irregularities (e.g., shimmer and jitter), as well as lexical-syntactic patterns.
An ablation study identified the most informative feature groups, enhancing the framework's clinical interpretability. This work provides a transparent approach to using speech analysis for mental health diagnostics.