Comparative Analysis of Spectrogram and MFCC Representations for Speech Emotion Recognition Using Machine Learning
Bibliographic record
Abstract
Emotion recognition is a key area of research within human-computer interaction, addressing the growing need for systems that can respond to human emotional states. While advancements have been made, challenges remain, particularly in selecting appropriate datasets, identifying effective audio features, and optimizing classification models. This study explores how different audio feature representations, specifically Mel-Frequency Cepstral Coefficients (MFCC) and spectrograms, influence the accuracy of emotion classification. By extracting these features from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and applying Random Forest (RF) and Support Vector Machine (SVM) classifiers, the research compares the performance of each feature-classifier pairing. Results indicate that RF and SVM classifiers with MFCC features achieved 50% accuracy, while spectrogram features led to 45% and 54% accuracy, respectively. These findings suggest that simpler models, when combined with appropriate features, can offer promising performance, contributing to more responsive and adaptive human-computer interaction applications.
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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 | 0.002 | 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 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".