One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme\n Conversion With a Transformer Ensemble
Bibliographic record
Abstract
The task of grapheme-to-phoneme (G2P) conversion is important for both speech\nrecognition and synthesis. Similar to other speech and language processing\ntasks, in a scenario where only small-sized training data are available,\nlearning G2P models is challenging. We describe a simple approach of exploiting\nmodel ensembles, based on multilingual Transformers and self-training, to\ndevelop a highly effective G2P solution for 15 languages. Our models are\ndeveloped as part of our participation in the SIGMORPHON 2020 Shared Task 1\nfocused at G2P. Our best models achieve 14.99 word error rate (WER) and 3.30\nphoneme error rate (PER), a sizeable improvement over the shared task\ncompetitive baselines.\n
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".