CNN-Transformer Based Speech Emotion Detection
Bibliographic record
Abstract
In this study, a parallel network technique trained on the Ryerson Audio-Visual Dataset of Speech and Song (RAVDESS) was used to perform an autonomous speech emotion recognition (SER) challenge to categorize four distinct emotions. To capture both spatial and temporal data, the architecture comprised attention-based networks with CNN-based networks that ran in tandem. Additive White Gaussian Noise (AWGN) was used as augmentation techniques for multiple folds to improve the model’s generalization. The model’s input was MFCC, which was created from the raw audio data. The MFCC were represented as images, with the height and breadth corresponding to the time and frequency dimensions of the MFCC, in order to take use of the proven effectiveness of CNNs in image classification. Transformer Encoder layer, an attention-based model, was used to capture temporal characteristics. The projects’ findings demonstrated that the Parallel CNN-Transformer network’s accuracy as 88.16% for 1-fold augmentation, 92.11% for 2-fold augmentation and 86.84% of accuracy for 3-fold augmentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".