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

• URL: http://arxiv.org/abs/2306.02579v1
• Date: Mon, 5 Jun 2023 04:10:04 GMT
• Title: Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model
• Authors: Hoyeon Lee, Hyun-Wook Yoon, Jong-Hwan Kim, Jae-Min Kim
• Abstract summary: Cross-lingual transfer learning can be particularly effective 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.
• Score: 13.730152819942445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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