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 rely
on carefully hand-crafted lexicons developed by experts. This poses a two-fold
problem. Firstly, the lexicons are generated using a fixed phoneme set,
usually, ARPABET or IPA, which might not be the most optimal way to represent
phonemes for all languages. Secondly, the man-hours required to produce such an
expert lexicon are very high. In this paper, we eliminate both of these issues
by using recent advances in self-supervised learning to obtain data-driven
phoneme representations instead of fixed representations. We compare our
lexicon-free approach against strong baselines that utilize a well-crafted
lexicon. Furthermore, we show that our data-driven lexicon-free method performs
as good or even marginally better than the conventional rule-based or
lexicon-based neural G2Ps in terms of Mean Opinion Score (MOS) while using no
prior language lexicon or phoneme set, i.e. no linguistic expertise.

通过使用自监督学习的最新进展，本文消除了使用固定表示而非数据驱动的方法生成专家词典的问题，将其应用于无词典的文本转语音系统中，与基于专家词典的方法相比，在没有语言专业知识的前提下达到甚至略优的声音质量。