Performance Analysis of CNN-Based Speech Emotion Recognition System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an innovative approach to Speech Emotion Recognition (SER) using Convolutional Neural Networks (CNNs), aiming to enhance human-computer interaction by enabling machines to recognize and respond to human emotions accurately. The study utilizes the Toronto Emotional Speech Set (TESS), a comprehensive dataset comprising approximately 2,800 utterances with diverse emotional states. The methodology involves pre-processing speech signals to extract Mel-Frequency Cepstral Coefficients, which are then converted into spectrograms and fed into a CNN model for emotion classification. The results demonstrate the model's robust performance in recognizing emotions such as anger, happiness, fear, and sadness with high precision and accuracy. The paper discusses the impact of various hyperparameters on model performance, highlighting the trade-offs between training time and accuracy. The findings underscore the potential of CNN-based approaches in SER, offering valuable insights for future research and applications in affective computing.
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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 | 0.001 |
| 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) | 0.000 | 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 it