ArticleChild and adolescent psychiatry and mental health2024
Differentiation between depression and bipolar disorder in child and adolescents by voice features.
Article in Child and adolescent psychiatry and mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Deep neural network-based analysis of voice biomarkers for monitoring treatment response in adolescent major depressive disorder.Communications medicine · 2026Article
- Voice-based machine learning for rapid screening of bipolar disorder and major depressive disorder in children and adolescents: a robust and low-complexity diagnostic model.BMC psychiatry · 2026Article
- A multimodal feature fusion model integrating voice acoustic features and early key risk factors for early screening of postpartum depression: a prospective short-term longitudinal observational study.Frontiers in public health · 2026Observational
- Mood states recognition based on Mandarin speech and deep learning in patients with bipolar disorder.BMC psychiatry · 2025Article
- Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening.Frontiers in psychiatry · 2024Article
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9 authors.
Funding
Abstract
objectiveMajor depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features.
methodsThis study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve.
resultsThe three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD.
conclusionBy exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.
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