标题： 混合G2P-T5：使用语音自监督学习和语言模型的多语种文本无G2P语音合成 显示英文标题

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

摘要： 本研究提出了一种新颖的语音合成方法，该方法可以通过使用基于深度学习的模型直接从语音生成离散标记来替代传统的音素转写（G2P）转换。 利用预训练的语音SSL模型，我们训练了一个T5编码器，以从混合脚本文本（例如，包含汉字和假名的文本）生成伪语言标签。 该方法消除了对手动语音转写的需求，降低了成本并提高了可扩展性，尤其是在大规模未转写的音频数据集上。 我们的模型在性能上与传统的基于G2P的文本到语音系统相当，并且能够合成保留自然语言和副语言特征的语音，例如口音和语调。 显示英文摘要

摘要： 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.