Speech based Emotion Recognition using Machine Learning
Bibliographic record
Abstract
Emotions help a lot in recognizing the feelings of a human being. As per the study, there are multiple ways such as Linguistic, Video, Physiological signals, Audio cues, etc. that help in analyzing emotions. In this paper, analysis of speech has been done as it is the most natural way of showing emotion. For the experiment, RA VDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) database is used that contains audio files of various people that represent discrete emotions. Based on speech data, a comparison of various machine learning classification algorithms has been done that assist in the categorization of multiple emotions. The compared algorithms use some classifiers that are Recurrent neural network (RNN), Support vector machine (SVM), K- Nearest Neighbors (k-NN), Adaboost, Gradient Boosting Classifier, Multi-Layer Perceptron (MLP), and Random Forest. The classifiers were analyzed extracted features such as Mel Frequency Cepstral Coefficients (MFCC), Chroma, Mel: Mel Spectrogram Frequency, Spectral Contrast, and Tonnetz available in librosa library of python language. After analysis, experimental results reveal that among all other classifiers most accurate approach for emotion recognition with 89.5% was achieved by MLP classifier.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".