Accepted for/Published in: JMIR Mental Health
Date Submitted: Mar 24, 2025
Open Peer Review Period: Mar 30, 2025 - May 25, 2025
Date Accepted: Jun 22, 2025
(closed for review but you can still tweet)
Speech Emotion Recognition in Mental Health: A Systematic Review of Voice Based Applications
ABSTRACT
Background:
The field of Speech Emotion Recognition (SER) encompasses a wide variety of approaches with AI technologies providing improvements in recent years. The links between subjects’ emotional states and pathological diagnoses are of particular interest. This study aims to investigate the performance of tools combining these approaches with a view to their use within clinical contexts.
Objective:
The objective of this review was to determine the extent to which SER technologies have already been applied within clinical contexts.
Methods:
The review includes studies applied to speech (audio) signal, for a select set of pathologies/disorders and only includes those studies that include an evaluation of diagnosis performance using machine learning performance metrics or statistical correlation measures. PubMed, IEEE Explore, ArXiv and Science Direct databases were queried, as recently as February 2025. The QUADAS framework was used to measure the Risk of Bias.
Results:
A total of 14 articles were included in the final review. The included papers addressed the suicide risk (3), depression (8), and psychotic disorders (3).
Conclusions:
SER technologies are mostly used indirectly in mental health research and in a wide variety of manners including different architectures, datasets and pathologies. This diversity makes a direct assessment of the technology challenging. Nonetheless, promising results are obtained in various studies that attempt to diagnose patients based on either indirect or direct results from SER models. These results highlight the potential for this technology to be used within a clinical setting. Future work should focus on how these technologies can be used collaboratively by clinicians. Clinical Trial: Prospero ID 1006669
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Copyright
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