ArticleFrontiers in neurology2024
Depressive and mania mood state detection through voice as a biomarker using machine learning.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Emerging trends and clinical challenges in AI-enhanced emotion diagnosis using physiological data.Medical & biological engineering & computing · 2026Review
- From Stress to Synapse: The Neuronal Atrophy Pathway to Mood Dysregulation.International journal of molecular sciences · 2025Review
- Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.Depression and anxiety · 2025Article
- Identifying depression with mixed features: the potential value of eye-tracking features.Frontiers in neurology · 2025Article
- Acoustic-based machine learning approaches for depression detection in Chinese university students.Frontiers in public health · 2025Article
- The burden of depressive disorder among the global 10-24 age group and the construction of an early risk factors model.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA). Results: Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%. Discussion: These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.
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