Learning a Parallel Network for Emotion Recognition Based on Small Training Data
Bibliographic record
Abstract
Speech emotion recognition (SER) classifies speech into emotion categories such as “happy”, “sad”, and “angry”. Speech emotion recognition has attracted more and more attention in recent years as a challenging pattern recognition task, but its performance is limited by the amount of training data. In this paper, we propose a parallel network consisting of a CNN and a Transformer that receives two types of inputs. The Convolutional Neural Network (CNN) accurately recognizes emotions from the speech data using a mel-spectrogram feature. The transformer uses Multi-Attention from Mel-Frequency Cepstrum Coefficient (MFCC) to realize the extraction of emotional semantic information in a sequence. Experiments are carried out on the Ryerson Audio-Visual Database of Emotion Speech and Song (RAVDESS) dataset. The results demonstrate the effectiveness of the proposed method and show significant improvement over previous results with fewer data and less training time without data augmentation.
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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 | 0.001 | 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) | 0.008 | 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".