Efficient Feature-Aware Hybrid Model of Deep Learning Architectures for Speech Emotion Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Robust automatic speech emotional-speech recognition architectures based on hybrid convolutional neural networks (CNN) and feedforward deep neural networks are proposed and named in this paper as: BFN, CNA, and HBN. BFN is a combination between bag-of-Audio-word (BoAW) and feedforward deep neural network, CNA based on CNN, finally, HBN is hybrid architecture between BFN and CNA. Overall accuracy is achieved by leveraging Mel-frequency cepstral coefficient features and bag-of-acoustic-words to feed the network, resulting in promising classification performance. In addition, the concatenated output from the proposed hybrid networks is fed into a softmax layer to produce a probability distribution over categorical classifications for speech recognition. The three proposed models are trained on eight emotional classes from the Ryerson Audio-Visual Database of Emotional Speech and Song audio (RAVDESS) dataset. Our proposed models achieved overall precision between 81.5% and 85.5% and overall accuracy between 80.6% and 84.5%, hence outperforming state-of-the-art models using the same dataset.
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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 | 0.000 |
| 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) | 0.000 | 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