Speech Audio Analytics based Classification of Human Emotions using Machine Learning and Deep Learning Models
Bibliographic record
Abstract
Speech emotion recognition is a significant area of research with applications in human-computer interaction, affective computing, and psychological studies. This paper presents a comprehensive investigation into speech emotion recognition using the RAVDESS dataset and various machine learning and deep learning models. Our study focuses on data preprocessing, feature extraction, model implementation, and performance evaluation. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDEES) dataset is used for performing training and testing for the custom model and in order to improve the generalization of the model, noise and audio stretching are explicitly added. Specifically, the effectiveness of decision trees, K-nearest neighbors (KNN), multilayer perceptron (MLP) classifiers, recurrent neural networks (RNNs) including long short-term memory (LSTM) and gated recurrent units (GRU), and convolutional neural networks (CNNs) is measured for classifying emotions from audio data. Through a series of experiments and analysis, the most effective model is identified based on the insights of its performance.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".