Cross-Lingual Transfer Learning for Phrase Break Prediction with
  Multilingual Language Model
Hoyeon Lee Hyun-Wook Yoon Jong-Hwan Kim Jae-Min Kim
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.