Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care
A new study explores the use of perceptual speech features to support clinical decision-making in mental health care. The research analyzes acoustic and linguistic characteristics to identify associations with symptoms of depression, anxiety, and ADHD. The findings suggest a transparent approach to speech-based mental health assessment, utilizing machine learning techniques for interpretation.
- ▪The study presents a systematic feature-based analysis framework for mental health assessment.
- ▪It examines associations between speech features and validated symptom measures of depression, anxiety, and ADHD.
- ▪The framework reveals stable relationships between symptom severity and vocal irregularities, lexical-syntactic patterns, and affective tone.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24678 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.24678 (cs) [Submitted on 23 May 2026] Title:Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care Authors:Vassilis Lyberatos, Edmund G. Dervakos, Eleni Adamidi, Athanasios Voulodimos, Giorgos Stamou View a PDF of the paper titled Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care, by Vassilis Lyberatos and 4 other authors View PDF Abstract:Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues.
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