Speech Emotion Classification using Ensemble Models with MFCC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Speech is one of the most promising features that reflects the underlying emotion of a human being. There are some measurable parameters in speech signals that reveal a persons affective state. Speech Emotion Recognition (SER) is a process of identifying the emotional elements in communication regardless of contextual relevance. Leveraging studies have taken place in this area. This paper proposes an ensemble model to automatically classify emotion from speech signals to one among the seven emotional classes neutral, calm, angry, sad, happy, fear, disgust, and surprised. In this work, speech spectral features have been extracted using Mel Frequency Cepstral Coefficient (MFCC). An emotion classification model based on 2-Dimensional Convolutional Neural Networks (2D-CNN) and eXtreme Gradient Boosting (XG-Boost) is proposed in this paper. This work also compares the performance of the proposed ensemble model with baseline models and other ensemble models. The accuracy of each model on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset is computed and the proposed model shows maximum accuracy in classifying emotions.
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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 | 0.002 |
| 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 | 0.000 |
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