Speech Emotion Recognition using Fully Convolutional Network and Augmented RAVDESS Dataset
Bibliographic record
Abstract
Speech Emotion Recognition (SER) refers to the process of determining an individual’s emotional state based on their spoken utterance. The success of SER will enhance the quality of human-machine interaction. It will enable the machine to understand and respond to the emotional content conveyed through speech. In this study, we adopted a framework which first augments the number of training samples in the dataset by a factor of 5 to increase the training data quantity and then extracts five informative features, namely Mel-Frequency Cepstral Coefficients (MFCC), Mel Spectrograms, Chroma features, Zero Crossing Rate (ZCR), and Root Mean Square (RMS) for an audio signal. A CNN- classifier with 3 dense layers (FCN) is used to accurately recognize emotions in speech. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) is used for conducting the experiments. Experimental results showed that augmented RAVDESS dataset, achieved an accuracy of 92%, which is approximately 30% higher than when tested on the non-augmented RAVDESS dataset. Compared with the baseline model, adopted frameworks achieved 3% higher SER accuracy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.003 |
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".