Emotion Detection from Speech Signals using Voting Mechanism on Classified Frames
Bibliographic record
Abstract
Understanding human emotion is a complicated task for humans themselves, however, this did not stop the researchers from trying to make machines capable of understanding human emotions. Many approaches have been followed, using speech signals to detect emotions has been popular among these approaches. In this study, Mel Frequency Cepstrum Coefficient (MFCC) features were extracted from speech signals to detect the underlying emotion of the speech. Extracted features were used to classify different emotions using LMT classifier. For each frame of a speech signal, 13-dimensional feature vectors were extracted and Logistic Model Tree (LMT) models were trained using these features. For classifying an unknown speech signal, the 13-dimensional frame features are first extracted from the signal and each frame is classified using the trained model. Using a voting mechanism on the classified frames, the emotion of the speech signal is detected. Experimental results on two datasets- Berlin Database of Emotional Speech (Emo-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) show that our approach works very well in classifying certain emotions while it struggles to discern the differences between some pairs of emotions. Among the trained models, the maximum accuracy achieved was 70% in detecting 7 different emotions. Considering the small dimension size of the feature vectors used, this approach provides an efficient solution to classifying different emotions using speech signals.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".