Comparative Analysis of Features In a Speech Emotion Recognition System using Convolutional Neural Networks
Bibliographic record
Abstract
In the past decade, Speech Emotion Recognition (SER) in many spoken languages has become a field of growing interest. MFCCs (Mel Frequency Cepstrum Coefficients) are commonly utilized representations for audio classification, and are now becoming a prominent feature in SER systems. However, in the view of a performance analysis, there exists another feature named PCEN (Per Channel Energy Normalization) that has proven to outperform MFCCs in the context of speech. In order to compare the performances of the MFCC and PCEN, they have individually been used as inputs into a one dimensional Convolutional Neural Network (CNN). The samples from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) were utilized. Furthermore, the framework proposed in this paper obtains an accuracy of 85.3% for the configuration that utilizes PCEN, 77.4% for the configuration that uses only the MFCCs as inputs, and 78.1% that combines both.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".