UR-BERT：通过通用罗马化和语音标记预测扩展大规模多语言TTS的文本编码器
UR-BERT: Scaling Text Encoders for Massively Multilingual TTS Through Universal Romanization and Speech Token Prediction
AI总结：
提出UR-BERT，一种基于罗马化转录的TTS编码器，通过统一书写系统为罗马化表示，结合语音标记预测目标，在495种语言上实现高效多语言TTS，优于现有基线并泛化到未见语言。
摘要：
我们提出UR-BERT，一种基于罗马化转录的文本到语音（TTS）编码器，用于大规模多语言TTS系统。传统的字素到音素（G2P）方法由于可靠G2P资源的可用性，仅限于约100种语言。相比之下，UR-BERT通过将多样化的书写系统统一为共享的罗马化表示，扩展到495种语言。为了进一步增强语音保真度和文本-语音对齐，我们在训练过程中引入了一个语音标记预测目标，这促使编码器以数据高效的方式学习语音感知的语音表示。实验表明，基于UR-BERT构建的TTS系统在广泛的语言和资源条件下，始终优于最近的文本编码器基线，并展现出对未见语言的强大泛化能力。
英文摘要：
We propose UR-BERT, a Romanized transcription-based text-to-speech (TTS) encoder for massively multilingual TTS systems. Conventional grapheme-to-phoneme (G2P)-based approaches are limited to around 100 languages due to the availability of reliable G2P resources. In contrast, UR-BERT scales to 495 languages by unifying diverse writing systems into a shared Romanization representation. To further enhance phonetic fidelity and text-speech alignment, we introduce a speech token prediction objective during training, which encourages the encoder to learn speech-aware phonetic representations in a data-efficient manner. Experiments show that TTS systems built on UR-BERT consistently outperform recent text encoder baselines across a wide range of languages and resource conditions, and demonstrate strong generalization to unseen languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
