  Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech
(TTS) pipeline. However, G2P conversion is difficult for languages that contain
heteronyms -- words that have one spelling but can be pronounced in multiple
ways. G2P datasets with annotated heteronyms are limited in size and expensive
to create, as human labeling remains the primary method for heteronym
disambiguation. We propose a RAD-TTS Aligner-based pipeline to automatically
disambiguate heteronyms in datasets that contain both audio with text
transcripts. The best pronunciation can be chosen by generating all possible
candidates for each heteronym and scoring them with an Aligner model. The
resulting labels can be used to create training datasets for use in both
multi-stage and end-to-end G2P systems.
