Real-time Stress Detection Model and Voice Analysis: An Integrated VR-based Game for Training Public Speaking Skills
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a work in progress Virtual Reality (VR) based serious game, integrated with an algorithmic classification model for detecting stress during public speaking in real-time by analysing the speaker's voice. The designed VR game offers real-time virtual social support/feedback for the training of public speaking skills. We developed a stress detection model that recognises the stress and an altered normal confident state based on 24 actors' voice expressions from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Three different classifiers models were constructed for each actor by extracting and identifying overall significant voice features. The results show that random forest classification using features Amplitude Envelope (AE), Root-Mean-Square (RMS) and Mel-Frequency Cepstral Coefficients (MFCCs) provides high accuracy for detection of stress, while many more features can possibly be explored.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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 it