Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916055245389824 |
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| author | Lyberatos, Vassilis Dervakos, Edmund G. Adamidi, Eleni Voulodimos, Athanasios Stamou, Giorgos |
| author_facet | Lyberatos, Vassilis Dervakos, Edmund G. Adamidi, Eleni Voulodimos, Athanasios Stamou, Giorgos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24678 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care Lyberatos, Vassilis Dervakos, Edmund G. Adamidi, Eleni Voulodimos, Athanasios Stamou, Giorgos Artificial Intelligence Computation and Language Sound 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. |
| title | Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care |
| topic | Artificial Intelligence Computation and Language Sound |
| url | https://arxiv.org/abs/2605.24678 |