SER: Speech Emotion Recognition Application Based on Extreme Learning Machine
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays, device control is commonly using the human body feature or voice recognition technology. To expand the functionality of voice recognition, plenty of researchers have developed speech emotion recognition. By recognizing sound emotions, a system can provide better and beneficial decision-making output. This paper describes the development of an application that is able to recognize speech emotions using Extreme Learning Machine (ELM). We use the dataset from Toronto Emotional Speech Set (TESS). The dataset contains 2800 data points (audio files) in total and has high quality audio that focused on female voices to ensure the reliability of the data. The Speech Emotion Recognition application was design as web-based application that used Golang and Python which built with Extreme Learning Machine and Random Forest to recognize speech emotions. As a result, the functionality test shows that the application was able to satisfy 6 out of 6 requirements, and the accuracy test shows an accuracy value of 100% by identifying 70 out of 70 test data.
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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 | 0.000 | 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) | 0.000 | 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 it