Speech-Based Emotion Recognition Using Acoustic Features and Neural Networks
Bibliographic record
Abstract
Speech Emotion Recognition (SER) enables systems to identify emotions from spoken language by analyzing acoustic features like pitch and intensity. This paper presents an SER system using the Toronto Emotional Speech Set (TESS) to classify seven emotions: anger, disgust, fear, neutral, happiness, pleasant surprise, and sadness. Features were extracted using Mel-Frequency Cepstral Coefficients (MFCCs) and classified using Random Forest model with precision as 1.00, recall as 0.98, 97.5% accuracy and CNN model with 99.64% accuracy. An interactive application built with Streamlit enables real-time emotion recognition, demonstrating the system's potential for enhancing human-computer interactions. The experimental results demonstrate the potential of the proposed SER system to accurately classify emotions, paving the way for more empathetic and natural interactions in technology.
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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) | 0.001 | 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".