Transformer-Based Multi-Head Attention for Noisy Speech Recognition
Bibliographic record
Abstract
This paper presents an advanced transformer-based multi-head attention approach for automatic speech recognition (ASR) in noisy speech environments. The study focuses on four distinct noise scenarios: subway, babble, car, and exhibition hall, each rigorously evaluated across different signal-to-noise ratios (SNRs) and a clean condition. Utilizing the AURORA 2 dataset, this research investigates the performance and robustness of the proposed model under these varied acoustic conditions. To further enhance the transformer-based attention’s performance in ASR, we compare its effectiveness against sound classification in environmental noise using the ESC-50 dataset. The experimental results provide a comprehensive analysis, demonstrating that the model with transformer-based multi-head attention, incorporating merging and gammatone frequency cepstral coefficients (GFCC) features, significantly outperforms a baseline model using GFCC features with transformer-based multi-head attention alone, without merging. The model achieves an accuracy of 98.75% on the AURORA 2 dataset in clean conditions, and maintains a high accuracy of 92.38% under noisy conditions. When evaluated on the ESC-10 dataset, it achieves an accuracy of 81.25%, underscoring its robustness and potential for practical deployment in diverse acoustic settings.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".