Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank\n Transformer
Bibliographic record
Abstract
Highly performing deep neural networks come at the cost of computational\ncomplexity that limits their practicality for deployment on portable devices.\nWe propose the low-rank transformer (LRT), a memory-efficient and fast neural\narchitecture that significantly reduces the parameters and boosts the speed of\ntraining and inference for end-to-end speech recognition. Our approach reduces\nthe number of parameters of the network by more than 50% and speeds up the\ninference time by around 1.35x compared to the baseline transformer model. The\nexperiments show that our LRT model generalizes better and yields lower error\nrates on both validation and test sets compared to an uncompressed transformer\nmodel. The LRT model outperforms those from existing works on several datasets\nin an end-to-end setting without using an external language model or acoustic\ndata.\n
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".