Performance Analysis of Human Emotion via Speech Recognition using Convolution Neural Network Algorithm compared with Hidden Markov Model Classifier for Improved Accuracy
Bibliographic record
Abstract
This research intends to use a new Convolution Neural Network method, as opposed to the old Hidden Markov Model (HMM) algorithm, to make better predictions about people's conveying feelings by the noises they make. The dataset for this research is taken from Toronto Emotional Speech Set (TESS). Using G-power 0.8, we determined the sample size for each dataset. The prediction of human emotion identification from voice signals is performed using either a Convolution Neural Network or a Hidden Markov Model, both of which need the same number of data samples (N=10). The suggested Convolutional Neural Network performs significantly better with the accuracy rate of 93.68% than the accuracy obtained by the Hidden Markov Model Classifier, which infers Convolutional Neural Network performs better. The significance level obtained by the investigation was p = 0.001 (p<0.05) and the two groups are statistically significant. When comparing the two models' performance in human emotion categorization using voice data, the suggested CNN model outperforms the Hidden Markov Model (HMM).
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| 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".