MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model

• URL: http://arxiv.org/abs/2509.01391v1
• Date: Mon, 01 Sep 2025 11:36:37 GMT
• Title: MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model
• Authors: Joonyong Park, Daisuke Saito, Nobuaki Minematsu,
• Abstract summary: This study presents a novel approach to voice synthesis that can substitute the traditional grapheme-to-phoneme (G2P) conversion.<n>We train a T5 encoder to produce pseudo-language labels from mixed-script texts.<n>Our model matches the performance of conventional G2P-based text-to-speech systems and is capable of synthesizing speech that retains natural linguistic and paralinguistic features.
• Score: 17.060696046727962
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This study presents a novel approach to voice synthesis that can substitute the traditional grapheme-to-phoneme (G2P) conversion by using a deep learning-based model that generates discrete tokens directly from speech. Utilizing a pre-trained voice SSL model, we train a T5 encoder to produce pseudo-language labels from mixed-script texts (e.g., containing Kanji and Kana). This method eliminates the need for manual phonetic transcription, reducing costs and enhancing scalability, especially for large non-transcribed audio datasets. Our model matches the performance of conventional G2P-based text-to-speech systems and is capable of synthesizing speech that retains natural linguistic and paralinguistic features, such as accents and intonations.

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