ArticleInternational journal of environmental research and public health2022
Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2025Pooled it
- Cross-Cultural Identification of Acoustic Voice Features for Depression: A Cross-Sectional Study of Vietnamese and Japanese Datasets.Bioengineering (Basel, Switzerland) · 2025Article
- Development of an objective early detection model for depressive symptoms using voice emotion analysis technology: empirical prospective cohort study among call center operators.Journal of occupational health · 2025Article
- Detection of Chronic Musculoskeletal Pain Using Voice Characteristics.IEEE journal of translational engineering in health and medicine · 2025Article
- Discrimination of mild cognitive impairment based on involuntary changes caused in voice elements.Frontiers in neurology · 2023Article
- Acoustic speech features are associated with late-life depression and apathy symptoms: Preliminary findings.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In general, it is common knowledge that people's feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician's diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.
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Registered trials
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