Recognizing Speech Emotion Based on Acoustic Features Using Machine Learning
Bibliographic record
Abstract
Detecting emotion from speech can be helpful to understand the state of individual's mind. Accurately classifying emotion from speech is a very challenging job. In this work, we combined two datasets- Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set (TESS) to diversify the speech dataset. The resulting dataset contains 4048 audio files. Seven key emotions of human have been considered for classification including happy, angry, sad, neutral, fearful, disgust, and surprised. 180 speech features have been extracted from audio files utilizing Mel-Frequency Cepstral Coefficient (MFCC), Chroma, and Mel Spectrogram techniques. We applied several traditional classifiers on the combined dataset as well as RAVDESS and TESS datasets separately. Comparative investigation shows that Gradient Boosting outperforms other classifiers on the combined dataset with an accuracy of 84.96%. Also, MLP classifier performs better on all three datasets compared to other classifiers. We believe that this study can contribute to the avenue of human-computer interaction as well as other applications by more precise emotion recognition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.010 | 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".