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The task of grapheme-to-phoneme (G2P) conversion is important for both speechrecognition and synthesis. Similar to other speech and language processingtasks, in a scenario where only small-sized training data are available,learning G2P models is challenging. We describe a simple approach of exploitingmodel ensembles, based on multilingual Transformers and self-training, todevelop a highly effective G2P solution for 15 languages. Our models aredeveloped as part of our participation in the SIGMORPHON 2020 Shared Task 1focused at G2P. Our best models achieve 14.99 word error rate (WER) and 3.30phoneme error rate (PER), a sizeable improvement over the shared taskcompetitive baselines.

One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble