To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation
Bibliographic record
Abstract
Arabic is known to present unique challenges for Automatic Speech Recognition (ASR).On one hand, its rich linguistic diversity and wide range of dialects complicate the development of robust, inclusive models.On the other, current multilingual ASR models are compute-intensive and lack proper comprehensive evaluations.In light of these challenges, we distill knowledge from large teacher models into smaller student variants that are more efficient.We also introduce a novel human-annotated dataset covering five under-represented Arabic dialects for evaluation.We further evaluate both our models and existing SoTA multilingual models on both standard available benchmarks and our new dialectal data.Our best-distilled model's overall performance (45.0%WER) surpasses that of a SoTA model twice its size (SeamlessM4T-large-v2, WER=47.0%) and its teacher model (Whisper-large-v2, WER=55.1%), and its average performance on our new dialectal data (56.9%WER) outperforms all other models.To gain more insight into the poor performance of these models on dialectal data, we conduct an error analysis and report the main types of errors the different models tend to make.The GitHub repository for the project is available at https: //github.com/UBC-NLP/distill-whisper-ar.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".