Efficient Feature-Aware Hybrid Model of Deep Learning Architectures for Speech Emotion Recognition
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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".