Determining Emotion Intensities from Audio Data Using Ensemble Models: A Late Fusion Approach
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an ensemble model in the determination of manifestation of emotion intensities from audio-dataset. An emotion denotes the mental state of the human mind or/and thought processes that represents a recognizable pattern of an entity like emotion arousal having a good similarity with its manifestation of vocal, facial or/and bodily signals. In this paper, we propose a stacking, late fusion approach where the best experimental outcome from two base models build from Random Forests and Extreme Gradient Boost are combined using simple majority voting. RAVDESS audio datasets, a public gender balanced dataset built by Ryerson University of Canada for the purpose of emotion study was used. 80% of the dataset was used for training while 20% was used for testing. Two features, MFCC and Chroma were introduced to the base models in a series of experimental setups and the outcome evaluated using confusion matrix, precision, recall and F1-Score. It was then compared to two state-of-the-art works done on KBES and RAVDESS datasets. This approach yielded an overall classification accuracy of 93%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it