Speech Emotion Recognition System
Bibliographic record
Abstract
This project describes "VoiEmo- A Speech Emotion Recognizer", a system for recognizing the emotional state of an individual from his/her speech. For example, one's speech becomes loud and fast, with a higher and wider range in pitch, when in a state of fear, anger, or joy whereas human voice is generally slow and low pitched in sadness and tiredness. We have particularly developed a classification model speech emotion detection based on Convolutional neural networks (CNNs), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Classification which make predictions considering the acoustic features of speech signal such as Mel Frequency Cepstral Coefficient (MFCC). Our models have been trained to recognize seven common emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). For training and testing the model, we have used relevant data from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and the Toronto Emotional Speech Set (TESS) Dataset. The system is advantageous as it can provide a general idea about the emotional state of the individual based on the acoustic features of the speech irrespective of the language the speaker speaks in, moreover, it also saves time and effort. Speech emotion recognition systems have their applications in various fields like in call centers and BPOs, criminal investigation, psychiatric therapy, the automobile industry, etc.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.059 |
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".