Self-supervised learning (SSL) has been dramatically successful not only in
monolingual but also in cross-lingual settings. However, since the two settings
have been studied individually in general, there has been little research
focusing on how effective a cross-lingual model is in comparison with a
monolingual model. In this paper, we investigate this fundamental question
empirically with Japanese automatic speech recognition (ASR) tasks. First, we
begin by comparing the ASR performance of cross-lingual and monolingual models
for two different language tasks while keeping the acoustic domain as identical
as possible. Then, we examine how much unlabeled data collected in Japanese is
needed to achieve performance comparable to a cross-lingual model pre-trained
with tens of thousands of hours of English and/or multilingual data. Finally,
we extensively investigate the effectiveness of SSL in Japanese and demonstrate
state-of-the-art performance on multiple ASR tasks. Since there is no
comprehensive SSL study for Japanese, we hope this study will guide Japanese
SSL research.

本研究比较跨语言模型和单语言模型在日语自动语音识别上的表现，证明通过使用无标签日语数据，可实现与预先训练仅使用英语和 / 或多语言数据的跨语言模型相当的性能，并在多项自动语音识别任务上展示自监督学习在日语中的最新成果。