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Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech(TTS) pipeline. However, G2P conversion is difficult for languages that containheteronyms -- words that have one spelling but can be pronounced in multipleways. G2P datasets with annotated heteronyms are limited in size and expensiveto create, as human labeling remains the primary method for heteronymdisambiguation. We propose a RAD-TTS Aligner-based pipeline to automaticallydisambiguate heteronyms in datasets that contain both audio with texttranscripts. The best pronunciation can be chosen by generating all possiblecandidates for each heteronym and scoring them with an Aligner model. Theresulting labels can be used to create training datasets for use in bothmulti-stage and end-to-end G2P systems.

Automatic Heteronym Resolution Pipeline Using RAD-TTS Aligners