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Grapheme-to-Phoneme (G2P) is an essential first step in any modern,high-quality Text-to-Speech (TTS) system. Most of the current G2P systems relyon carefully hand-crafted lexicons developed by experts. This poses a two-foldproblem. Firstly, the lexicons are generated using a fixed phoneme set,usually, ARPABET or IPA, which might not be the most optimal way to representphonemes for all languages. Secondly, the man-hours required to produce such anexpert lexicon are very high. In this paper, we eliminate both of these issuesby using recent advances in self-supervised learning to obtain data-drivenphoneme representations instead of fixed representations. We compare ourlexicon-free approach against strong baselines that utilize a well-craftedlexicon. Furthermore, we show that our data-driven lexicon-free method performsas good or even marginally better than the conventional rule-based orlexicon-based neural G2Ps in terms of Mean Opinion Score (MOS) while using noprior language lexicon or phoneme set, i.e. no linguistic expertise.

Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech