ArticleScientific reports2024
Depression recognition using voice-based pre-training model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Speech Depression Screening via Multi-Scale Feature Enhancement and Emotion-Aware Contrastive Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Speech as a biomarker for supported diagnosis of major depressive disorder using self-supervised representations.Nature communications · 2026Article
- Large Language Models for Depression Detection: A Review with Prospects of Incomplete Multimodality.Brain sciences · 2026Review
- Reinforcement learning from human feedback improves automatic speech recognition models for ancient Thai language.Scientific reports · 2026Article
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Artificial intelligence in genomic medicine: dispelling three myths.NPJ genomic medicine · 2026Article
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026Article
- Deep neural network-based analysis of voice biomarkers for monitoring treatment response in adolescent major depressive disorder.Communications medicine · 2026Article
- HMDF-Net: a transfer learning-based heterogeneous multimodal dynamic fusion network for depression detection among inmates in correctional facilities.Frontiers in psychiatry · 2026Article
- Article
- Ecological Momentary Assessment and Voice-Informed Forecast and Detection for the Diagnosis of Major Depression.Advances in experimental medicine and biology · 2026Review
- Deep spectrotemporal network based depression severity estimation from speech.Scientific reports · 2025Article
- Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.JMIR medical informatics · 2025Article
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- Depression detection methods based on multimodal fusion of voice and text.Scientific reports · 2025Article
- Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.Depression and anxiety · 2025Article
- Intimate partner violence and stress-related disorders: from epigenomics to resilience.Frontiers in global women's health · 2025Review
- Next-generation wearable sensors for biopsychosocial care in mental health: a narrative review.BMJ digital health & AI · 2025Review
- Review
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Authors and funding
7 authors.
Funding
Abstract
The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.
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Registered trials
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