Phrase break prediction is a crucial task for improving the prosody
naturalness of a text-to-speech (TTS) system. However, most proposed phrase
break prediction models are monolingual, trained exclusively on a large amount
of labeled data. In this paper, we address this issue for low-resource
languages with limited labeled data using cross-lingual transfer. We
investigate the effectiveness of zero-shot and few-shot cross-lingual transfer
for phrase break prediction using a pre-trained multilingual language model. We
use manually collected datasets in four Indo-European languages: one
high-resource language and three with limited resources. Our findings
demonstrate that cross-lingual transfer learning can be a particularly
effective approach, especially in the few-shot setting, for improving
performance in low-resource languages. This suggests that cross-lingual
transfer can be inexpensive and effective for developing TTS front-end in
resource-poor languages.

本文采用跨语言迁移学习的方式，使用预训练的多语言语言模型，在少量标注数据的情况下，研究了短语断点预测的效果，发现跨语言迁移可以是一种有效的方法，尤其是在低资源语言中，因此建议使用跨语言迁移来改善语音合成前端。

多语言语言模型的跨语言短语断点预测迁移学习

Cross-Lingual Transfer Learning for Phrase Break Prediction with  Multilingual Language Model

This paper presents our work on phrase break prediction in the context of
end-to-end TTS systems, motivated by the following questions: (i) Is there any
utility in incorporating an explicit phrasing model in an end-to-end TTS
system?, and (ii) How do you evaluate the effectiveness of a phrasing model in
an end-to-end TTS system? In particular, the utility and effectiveness of
phrase break prediction models are evaluated in in the context of childrens
story synthesis, using listener comprehension. We show by means of perceptual
listening evaluations that there is a clear preference for stories synthesized
after predicting the location of phrase breaks using a trained phrasing model,
over stories directly synthesized without predicting the location of phrase
breaks.

该论文研究了在端到端 TTS 系统的上下文中的短语断句预测问题，并评估了在儿童故事合成中使用预测短语断句的实用性和有效性，结果表明使用训练好的短语模型预测短语断句的故事比直接合成的故事更受听众理解。