  Grapheme-to-Phoneme (G2P) models convert words to their phonetic
pronunciations. Classic G2P methods include rule-based systems and
pronunciation dictionaries, while modern G2P systems incorporate learning, such
as, LSTM and Transformer-based attention models. Usually, dictionary-based
methods require significant manual effort to build, and have limited adaptivity
on unseen words. And transformer-based models require significant training
data, and do not generalize well, especially for dialects with limited data.
  We propose a novel use of transformer-based attention model that can adapt to
unseen dialects of English language, while using a small dictionary. We show
that our method has potential applications for accent transfer for
text-to-speech, and for building robust G2P models for dialects with limited
pronunciation dictionary size.
  We experiment with two English dialects: Indian and British. A model trained
from scratch using 1000 words from British English dictionary, with 14211 words
held out, leads to phoneme error rate (PER) of 26.877%, on a test set generated
using the full dictionary. The same model pretrained on CMUDict American
English dictionary, and fine-tuned on the same dataset leads to PER of 2.469%
on the test set.
