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

• URL: http://arxiv.org/abs/2401.10465v1
• Date: Fri, 19 Jan 2024 03:37:27 GMT
• Title: Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
• Authors: Abhinav Garg, Jiyeon Kim, Sushil Khyalia, Chanwoo Kim, Dhananjaya Gowda
• Abstract summary: 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. 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.
• Score: 11.76320241588959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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