Multi-Dialect Speech Recognition in English Using Attention on Ensemble of Experts
Bibliographic record
Abstract
In the presence of a wide variety of dialects, training dialect-specific models for each dialect is a demanding task. Previous studies have explored training a single model that is robust across multiple dialects. These studies have used either multi-condition training, multi-task learning, end-to-end modeling, or ensemble modeling. In this study, we further explore using a single model for multi-dialect speech recognition using ensemble modeling. First, we build an ensemble of dialect-specific models (or experts). Then we linearly combine the outputs of the experts using attention weights generated by a long short-term memory (LSTM) network. For comparison purposes, we train a model that jointly learns to recognize and classify dialects using multi-task learning and a second model using multi-condition training. We train all of these models with about 60,000 hours of speech data collected in American English, Canadian English, British English, and Australian English. Experimental results reveal that our best proposed model achieved an average 4.74% word error rate reduction (WERR) compared to the strong baseline model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".