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