Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a systematic, interpretable framework for analyzing speech features related to mental health, which combines acoustic and linguistic characteristics to enhance clinical decision-making. This work represents a significant advancement in the application of machine learning to mental health care, providing a transparent approach that could foster greater acceptance and integration of AI technologies in clinical practice.
Methodology
The methodology presented in the paper is robust and comprehensive, integrating various features from both acoustic and linguistic perspectives to analyze mental health through speech. The use of interpretable machine learning techniques such as XGBoost combined with SHAP and LIME for feature importance analysis is commendable, as it aligns with the growing demand for explainable AI in clinical settings. The systematic feature extraction process, which includes both traditional statistical methods and modern neural models, enhances the interpretability and clinical relevance of the findings. However, the paper could benefit from clearer descriptions of the feature extraction algorithms and their implementation details.
Results & evaluation
The experiments conducted across multiple datasets (StressID, DAIC-WOZ, Androids, EATD, and a proprietary dataset) demonstrate the framework's applicability in various contexts. The reported performance metrics, such as accuracy and F1-scores, are promising, particularly the strong performance on the Androids dataset. The ablation study effectively identifies the most informative feature groups, providing insights into which features are most predictive of mental health conditions. However, the variability in results across datasets suggests that further validation is needed to establish the generalizability of the findings.
Reproducibility
The paper lacks sufficient details regarding the implementation of the methodologies, particularly in terms of code availability and specific parameter settings for the models used. While the feature extraction process is well-documented, the absence of a clear reproducibility section or supplementary materials makes it challenging for other researchers to replicate the results. Providing a GitHub repository or similar resource would significantly enhance reproducibility.
Limitations
The study acknowledges several limitations, including the challenges posed by confounding factors such as background noise and recording conditions, which may affect the robustness of the model. Additionally, the reliance on questionnaire-based cutoffs for classification could introduce variability in the results. The paper also notes that the models may not capture temporal dynamics effectively, which is crucial for understanding the progression of mental health symptoms.
Broader impact
This research has significant implications for the field of mental health assessment, as it provides a framework for leveraging speech analysis in clinical decision-making. The integration of explainable AI into mental health diagnostics could enhance clinician trust and facilitate more accurate assessments. Furthermore, the findings may contribute to the development of scalable, non-invasive tools for monitoring mental health in real-world settings, potentially improving access to care. The main contribution of this paper is the introduction of a systematic, interpretable framework for analyzing speech features related to mental health, which combines acoustic and linguistic characteristics to enhance clinical decision-making. This work represents a significant advancement in the application of machine learning to mental health care, providing a transparent approach that could foster greater acceptance and integration of AI technologies in clinical practice.
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