SYSTEMATIC REVIEW article
Front. Psychol.
Sec. Emotion Science
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1645860
This article is part of the Research TopicEmotional Intelligence AI in Mental HealthView all 10 articles
Speech analysis and speech emotion recognition in mental disease: a scoping review
Provisionally accepted
- 1Magna Græcia University, Catanzaro, Italy
- 2Universita degli Studi di Messina Dipartimento di Ingegneria, Messina, Italy
- 3Universita degli Studi di Messina Dipartimento di Scienze Politiche e Giuridiche, Messina, Italy
- 4Universita degli Studi di Messina Dipartimento di Scienze biomediche odontoiatriche e delle immagini morfologiche e funzionali, Messina, Italy
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Background: Mental disorders have a significant impact on many areas of people's life, particularly on affective regulation; thus, there is a growing need to find disease-specific biomarkers to improve early diagnosis. Recently, machine learning technology using speech analysis proved to be a promising field that could aid mental health assessments. Furthermore, as prosodic expressions of emotions are altered in many psychiatric conditions, some studies successfully employed a speech emotion recognition model (SER) to identify mental diseases. The aim of this paper is to discuss the utilization of speech analysis in diagnosis of mental disorders, with a focus on studies using SER system to detect mental illness. Method: We searched PubMed, Scopus and Google Scholar for papers published from 2014 to 2024. We conducted a preliminary search, which revealed papers on the topic. Finally, 12 studies met the inclusion criteria and were included in the review. Results: Findings confirmed the efficacy of speech analysis in distinguishing between patients from healthy subjects; moreover, the examined studies underlined that some mental illnesses are associated with specific voice patterns. Furthermore, results from studies employing speech emotion recognition system to detect mental disorders showed that emotions can be successfully used as an intermediary step for mental diseases detection, particularly for mood disorders. Conclusions: These findings support the implementing of speech signals analysis in mental health assessment: it is an accessible and non-invasive method which can provide earlier diagnosis and a higher treatment personalization.
Keywords: Speech analysis, Acoustic features, Speech emotion recognition, Mental Disorders, Schizophrenia, Depression
Received: 12 Jun 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 Lombardo, Esposito, Carbone, Serrano and Mento. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Clara Lombardo, clara.lombardo@unicz.it
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