日语自监督语音表示模型的语言依赖性探索
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models
AI总结：
本文通过日语ASR任务实证比较跨语言与单语自监督模型，探究所需日语数据量，并展示多项任务上的最先进性能，为日语SSL研究提供指导。
摘要：
自监督学习（SSL）不仅在单语环境中取得了巨大成功，在跨语言环境中也同样如此。然而，由于这两种设置通常被分别研究，很少有研究关注跨语言模型与单语模型相比的有效性。本文通过日语自动语音识别（ASR）任务，实证研究了这一基本问题。首先，我们在尽可能保持声学领域一致的前提下，比较了跨语言模型和单语模型在两种不同语言任务上的ASR性能。然后，我们考察了需要多少日语未标记数据才能达到与使用数万小时英语和/或多语言数据预训练的跨语言模型相当的性能。最后，我们广泛研究了SSL在日语中的有效性，并在多个ASR任务上展示了最先进的性能。由于目前尚无针对日语的全面SSL研究，我们希望这项研究能为日语SSL研究提供指导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
