Analysis of Human Emotion via Speech Recognition Using Viola Jones Compared with Histogram of Oriented Gradients (HOG) Algorithm with Improved Accuracy
Bibliographic record
Abstract
The objective of this study is to enhance the precision in predicting human emotions through speech signals.This is achieved by introducing a novel approach, the Viola Jones (VJ) method, in contrast to the conventionalHistogram of Oriented Gradients (HOG) algorithm. In this research we used Toronto Emotional Speech Set(TESS) as a dataset for this with a G-power of 0.8, alpha and beta values of 0.05 and 0.2, and a ConfidenceInterval of 95%, sample size is calculated as twenty (ten from Group 1 and ten from Group 2). Viola Jones(VJ) and Histogram of Oriented Gradients, both with the same amount of data samples (N=10), are used toperform the prediction of human emotion recognition from speech signals. The performance of the proposedviola jones is much greater than the accuracy rate of 88.65 percent achieved by the histogram of orientedgradients classifier. This is because the success rate of the proposed viola jones is 95.66 percent. The level ofsignificance that was assessed to be attained by the research was p = 0.001 (p<0.05) which infers the twogroups are statistically significant. For the performance evaluation of human emotion classification fromspeech data, the proposed Viola Jones (VJ) model achieves a greater level of precision than Histogram ofOriented Gradients (HOG).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".