Speech Emotion Recognition Using Machine Learning and Deep Learning
Bibliographic record
Abstract
In today's world, understanding and recognizing human emotions are crucial for how we interact with computers. Exactly finding emotion from speech is a very challenging job. We often use various cues like facial expressions, voice tone, and body movements to identify emotions. This study focuses on Speech Emotion Recognition, which means figuring out emotions from the way people speak. In this model, we have used two datasets the Ryerson Audio-visual Database of Emotional Speech and Song and the Toronto Emotional Speech Set both individually and in a combined dataset. The total number of datasets contains 4048 audio files and nine key emotions happy, calm, angry, sad, neutral, fearful, disgust, surprised, and pleasant surprise. This study employed three feature extraction techniques Mel-frequency cepstral coefficients, Chroma Short-Time Fourier Transform, and Mel spectrograms to generate 180 features from audio files. These features were used to train various classifiers on both the combined and individual datasets. It was found that the Convolutional Neural Network and Multi-Layer Perceptron classifiers outperformed the others on the combined dataset with an accuracy of 83.2 and 84.54 respectively. We believe that this study contributes significantly to human-computer interaction and other applications by enabling 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) | 0.007 | 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; both teacher heads agree on what is shown here.
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".