Speech and Text-based Depression Detection On Social Media For Early Intervention
Bibliographic record
Abstract
Depression is a significant mental health and SDG3 issue with far-reaching social and economic impacts. While early detection and intervention are crucial, traditional screening methods are often not accessible or accurate. This paper presents a method for detecting depression through a combination of voice and text analysis. By using log mel spectrograms and convolutional neural networks (CNNs) for voice analysis, and bi-directional long short-term memory (BiLSTM) networks for text analysis, the model improves detection accuracy and provides insights into mental health and well-being. The model structure, data collection, processing techniques, and training processes for both CNN and Bi-LSTM models are detailed. This study employs the "Suicide and Depression Detection" dataset from Kaggle for text and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for voice. A user interface is developed to ensure system accessibility. The experimental results demonstrate the high accuracy of the proposed approach for depression detection. This research highlights the importance of an accessible interface that combines multiple analysis methods and the need for interdisciplinary collaboration to address complex societal and mental health issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".