Speech Emotion Classification using Ensemble Models with MFCC
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".