ArticleScientific reports2025
Depression detection methods based on multimodal fusion of voice and text.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Generative AI emotion recognition from bodily gestures and vocal tone reveals modality-specific performance and positivity bias.Scientific reports · 2026Article
- Speech Depression Screening via Multi-Scale Feature Enhancement and Emotion-Aware Contrastive Learning.Bioengineering (Basel, Switzerland) · 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
- Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language.Frontiers in psychiatry · 2026Article
- Article
- Depression detection using deep learning and large language models from multimodalities.Frontiers in digital health · 2026Review
- SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition.BioData mining · 2025Article
- Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.JMIR medical informatics · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Depression is a prevalent mental health disorder, and early detection is crucial for timely intervention. Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for automated depression detection, leveraging bimodal data from voice and text. Wav2Vec 2.0 and BERT pre-trained models were utilized for feature extraction, while a multi-scale convolutional layer and Bi-LSTM network were employed for feature fusion and classification. Adaptive pooling was used to integrate features, enabling simultaneous depression classification and PHQ-8 severity estimation within a unified system.Experiments on the CMDC and DAIC datasets demonstrate the model's effectiveness. On CMDC, the F1 score improved by 0.0103 and 0.2017 compared to voice-only and text-only models, respectively, while RMSE decreased by 0.5186. On DAIC, the F1 score increased by 0.0645 and 0.2589, with RMSE reduced by 1.9901. These results highlight the proposed method's ability to capture and integrate multi-level information across modalities, significantly improving the accuracy and reliability of automated depression detection and severity prediction.
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