标题： 基于音素级预训练语言模型的说话人条件短语断点预测用于文本到语音 显示英文标题

标题： Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model

摘要： 本文推进了多说话人文本到语音（TTS）系统中的短语断裂预测（也称为分句）。我们通过利用说话人嵌入来整合说话人特定特征，以提高分句模型的性能。我们进一步证明，这些说话人嵌入仅从分句任务中就能捕捉到与说话人相关的特征。此外，我们通过少量样本适应方法探索了预训练说话人嵌入在未见说话人上的潜力。此外，我们开创性地将音素级预训练语言模型应用于这一TTS前端任务，显著提高了分句模型的准确性。我们的方法通过客观和主观评估进行了严格评估，证明了其有效性。 显示英文摘要

摘要： This paper advances phrase break prediction (also known as phrasing) in multi-speaker text-to-speech (TTS) systems. We integrate speaker-specific features by leveraging speaker embeddings to enhance the performance of the phrasing model. We further demonstrate that these speaker embeddings can capture speaker-related characteristics solely from the phrasing task. Besides, we explore the potential of pre-trained speaker embeddings for unseen speakers through a few-shot adaptation method. Furthermore, we pioneer the application of phoneme-level pre-trained language models to this TTS front-end task, which significantly boosts the accuracy of the phrasing model. Our methods are rigorously assessed through both objective and subjective evaluations, demonstrating their effectiveness.