Decoding Stress with Computer Vision-Based Approach Using Audio Signals for Psychological Event Identification during COVID-19
Bibliographic record
Abstract
Interpreting psychological events can be costly and quite complex. It is simple to translate such experiences into a person's spoken and nonverbal cues. The suggested model investigates a computer vision-based method for using an individual's audio signal to identify stressful psychological events. Different people's input speech signals are recorded and compared to the common questionnaire. A series of inquiries pertaining to the second stage of COVID-19 events are included in the questionnaire set. Through additional processing, these speech signals are converted into frequency components by means of the Fast Fourier transformation (FFT) method. A long short-term memory module processes each frequency component and produces temporal information from each frequency band. The features of speech signals are extracted into the temporal frames by this module. The VGG 16 algorithm is used to further classify each temporal frame into stress and un-stress classes. A classifier with 16 layers of architecture is called VGG 16. A feed-forward convolutional neural network called VGG 16 is used to divide the vast array of speech signal features into classes: stressed and unstressed. The proposed model attempts to recognize speech signals as stress indicators. A standard set of questionnaires with a series of interrogation-style questions has been used to develop the stress symptoms in an individual's mind. The audio signals generated by each person's responses are recorded and subsequently analyzed for stress and un-stress classes. The proposed model was able to identify stress in speech signals with 98% accuracy. The time and cost implications of the suggested model are relevant. Medical research is typically costly and time-consuming.LSTM; VGG 16; CNN model; data preprocessing; speech signal.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".