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Grapheme-to-Phoneme (G2P) models convert words to their phoneticpronunciations. Classic G2P methods include rule-based systems andpronunciation dictionaries, while modern G2P systems incorporate learning, suchas, LSTM and Transformer-based attention models. Usually, dictionary-basedmethods require significant manual effort to build, and have limited adaptivityon unseen words. And transformer-based models require significant trainingdata, and do not generalize well, especially for dialects with limited data.  We propose a novel use of transformer-based attention model that can adapt tounseen dialects of English language, while using a small dictionary. We showthat our method has potential applications for accent transfer fortext-to-speech, and for building robust G2P models for dialects with limitedpronunciation dictionary size.  We experiment with two English dialects: Indian and British. A model trainedfrom scratch using 1000 words from British English dictionary, with 14211 wordsheld out, leads to phoneme error rate (PER) of 26.877%, on a test set generatedusing the full dictionary. The same model pretrained on CMUDict AmericanEnglish dictionary, and fine-tuned on the same dataset leads to PER of 2.469%on the test set.

Grapheme-to-Phoneme Transformer Model for Transfer Learning Dialects