Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach
Bibliographic record
Abstract
In the field of mental health diagnostics, the acoustic characteristics of speech have been recognized as potent markers for the identification of depressive symptoms.This study harnesses the power of transfer learning (TL) to discern depression-related sentiments from speech.Acoustic features such as rhythm, pitch, and tone form the core of this analysis.The methodology unfolds in three distinct phases.Initially, a Multi-Layer Perceptron (MLP) network employing stochastic gradient descent is applied to the RAVDESS dataset, yielding an accuracy of 65%.This finding catalyzes the second phase, wherein a comprehensive hyperparameter optimization via grid search (GS) is conducted on the MLP Classifier.This step primarily focuses on detecting emotions commonly associated with depression, including neutrality, sadness, anger, fear, and disgust.The optimized MLP classifier indicates an improved accuracy of 71%.In the final phase, to enhance precision further, the same GS-based model, underpinned by TL principles, is applied to the TASS dataset.This application astonishingly achieves an accuracy of 99.80%, suggesting a high risk of depression.This comparative study establishes the proposed framework as a vanguard in the application of TL for depression prediction, showcasing a significant leap in accuracy over previous methodologies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".