MFCCs and TEO-MFCCs for Stress Detection on Women Gender through Deep Learning Analysis
Bibliographic record
Abstract
Men and women describe differing physical and emotional responses to stress; women reported experiencing it more than men with 11.7% higher. This issue has been affecting women in different ways than men due to biological and social factors (e.g., differences in hormone processes between both genders and dual responsibilities in the workplace as well as at home). This crucial issue raises many concerns about women’s mental health, and prolonged stress, such as heart problems, sleep problems, and others, will ideally impact them. Early stress detection is a crucial strategy to overcome the said problems since mental health issues always begin with stress problems. Therefore, in this paper, the MFCCs and TEOMFCCs for stress detection in the women’s gender through deep learning are presented. The stress classification had been made by utilizing the speech features, which are Mel Frequency Cepstral Coefficients (MFCCs) and Teager Energy Operator-Mel Frequency Cepstral Coefficients (TEO-MFCCs), with the help of Deep Learning technology, which is Convolutional Neural Networks (CNNs). The Toronto Emotional Speech Set (TESS) has been selected for this study since it consists of women’s speech data. The outcome shows that MFCCs provide better accuracy in predicting women’s stress, with a 98% score outperformed another study using the same dataset.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".