โก Quick Summary
This systematic review and meta-analysis evaluated the performance of Automatic Speech Analysis (ASA) in detecting depression, revealing a pooled accuracy of 0.81 and sensitivity of 0.84. While promising, ASA is currently best viewed as a complementary tool rather than a standalone diagnostic method.
๐ Key Details
- ๐ Dataset: 105 studies included in the analysis
- ๐งฉ Features used: Various speech features and eliciting tasks
- โ๏ธ Technology: Machine learning and deep learning approaches
- ๐ Performance: Highest accuracy: 0.81, Sensitivity: 0.84, Specificity: 0.83
๐ Key Takeaways
- ๐ ASA shows potential for detecting depression through speech analysis.
- ๐ก Pooled mean accuracy was found to be 0.81, indicating strong diagnostic performance.
- ๐ฉโ๐ฌ Sensitivity reached 0.84, suggesting a high rate of true positive detection.
- ๐ Specificity was also notable at 0.83, reflecting the method’s ability to correctly identify non-depressed individuals.
- ๐ค Various algorithms were assessed, highlighting the versatility of ASA in different contexts.
- ๐ The study emphasizes the need for further high-quality research to enhance ASA’s clinical applicability.
- ๐ Registration: The study is registered under PROSPERO CRD42023444431.
๐ Background
Depression is a prevalent mental health condition that often goes underdiagnosed, leading to significant personal and societal burdens. Traditional assessment methods can be subjective and inconsistent, prompting the exploration of innovative approaches like Automatic Speech Analysis (ASA). ASA leverages advancements in machine learning and deep learning to analyze speech patterns, offering a potentially objective means of assessing mental health.
๐๏ธ Study
This systematic review and meta-analysis involved a comprehensive search across eight databases, including MEDLINE and PsycInfo, from January 2013 to April 2025. The aim was to evaluate the diagnostic accuracy of ASA in detecting depression, focusing on studies that reported key performance metrics such as accuracy, sensitivity, and specificity. A total of 105 studies met the inclusion criteria, providing a robust dataset for analysis.
๐ Results
The findings revealed a pooled mean of the highest accuracy at 0.81 (95% CI 0.79 to 0.83), with sensitivity at 0.84 (95% CI 0.81 to 0.86) and specificity at 0.83 (95% CI 0.79 to 0.86). Conversely, the lowest accuracy was noted at 0.66 (95% CI 0.63 to 0.69), indicating variability in performance across different studies. These results underscore the potential of ASA as a diagnostic tool while also highlighting the need for further refinement.
๐ Impact and Implications
The implications of this study are significant for the field of mental health. ASA could serve as a valuable complementary method for depression detection, particularly in settings where traditional assessments may be challenging. By integrating ASA into clinical practice, healthcare providers could enhance their diagnostic capabilities, ultimately leading to improved patient outcomes. However, the current limitations suggest that ASA should not yet be relied upon as a standalone diagnostic tool.
๐ฎ Conclusion
This systematic review highlights the promising potential of Automatic Speech Analysis in detecting depression, with strong performance metrics indicating its utility in clinical settings. As research continues to evolve, it is crucial to conduct further high-quality studies to develop robust models that can be generalized across diverse populations. The future of mental health assessment may very well include innovative technologies like ASA, paving the way for more accurate and accessible diagnostics.
๐ฌ Your comments
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Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.
Abstract
BACKGROUND: Despite the high prevalence and significant burden of depression, underdiagnosis remains a persistent challenge. Automatic speech analysis (ASA) has emerged as a promising method for depression assessment. However, a comprehensive quantitative synthesis evaluating its diagnostic accuracy is still lacking.
OBJECTIVE: This systematic review and meta-analysis aimed to assess the diagnostic performance of ASA in detecting depression, considering both machine learning and deep learning approaches.
METHODS: We conducted a systematic search across 8 databases, including MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar from January 2013 to April 1, 2025. We included studies published in English that evaluated the accuracy of ASA for detecting depression, and reported performance metrics such as accuracy, sensitivity, specificity, precision, or confusion matrices. Study quality was assessed using a modified version of the Quality Assessment of Studies of Diagnostic Accuracy-Revised. A 3-level meta-analysis was performed to estimate the pooled highest and lowest accuracy, sensitivity, specificity, and precision. Meta-regressions and subgroup analyses were performed to explore heterogeneity across various factors, including type of publication, artificial intelligence algorithms, speech features, speech-eliciting tasks, ground truth assessment, validation approach, dataset, dataset language, participants’ mean age, and sample size.
RESULTS: Of the 1345 records identified, 105 studies met the inclusion criteria. The pooled mean of the highest accuracy, sensitivity, specificity, and precision were 0.81 (95% CI 0.79 to 0.83), 0.84 (95% CI 0.81 to 0.86), 0.83 (95% CI 0.79 to 0.86), and 0.81 (95% CI 0.77 to 0.84), respectively, whereas the pooled mean of the lowest accuracy, sensitivity, specificity, and precision were 0.66 (95% CI 0.63 to 0.69), 0.63 (95% CI 0.58 to 0.68), 0.60 (95% CI 0.55 to 0.66), and 0.64 (95% CI 0.58 to 0.70), respectively.
CONCLUSIONS: ASA shows promise as a method for detecting depression, though its readiness for clinical application as a standalone tool remains limited. At present, it should be regarded as a complementary method, with potential applications across diverse contexts. Further high-quality, peer-reviewed studies are needed to support the development of robust, generalizable models and to advance this emerging field.
TRIAL REGISTRATION: PROSPERO CRD42023444431; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023444431.
Author: [‘Maran PL’, ‘Braquehais MD’, ‘Vlaic A’, ‘Alonzo-Castillo MT’, ‘Vendrell-Serres J’, ‘Ramos-Quiroga JA’, ‘Rodrรญguez-Urrutia A’]
Journal: JMIR Ment Health
Citation: Maran PL, et al. Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis. Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis. 2025; 12:e67802. doi: 10.2196/67802