Bibliographic record
Abstract
Emotions are the best way for people to communicate their thoughts and actions to others. The most important technology in the world today is the ability to recognize emotions from a single speaker's voice. The ability to recognize emotions is very useful in gaining various insightful insights into a person's thoughts. The process of extracting emotions from human speech is called Speech Emotion Recognition (SER). We used the RAVDESS (Ryerson AudioVisual Database of Emotional Speech and Song) dataset to extract emotions from Speech. Emotions are extracted from speech based on speech parameters such as Mel-FrequencyCepstral -Coefficients (MFCC) and Mel Spectrogram. After training with a Multilayer Perceptron classifier (MLP), the obtained data had an accuracy of 68.33% and accuracy of 80.64% after training with Convolutional Neural Networks Long Short Term Memory (CNN LSTM).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".