This paper presents XLS-R, a large-scale model for cross-lingual speech
representation learning based on wav2vec 2.0. We train models with up to 2B
parameters on nearly half a million hours of publicly available speech audio in
128 languages, an order of magnitude more public data than the largest known
prior work. Our evaluation covers a wide range of tasks, domains, data regimes
and languages, both high and low-resource. On the CoVoST-2 speech translation
benchmark, we improve the previous state of the art by an average of 7.4 BLEU
over 21 translation directions into English. For speech recognition, XLS-R
improves over the best known prior work on BABEL, MLS, CommonVoice as well as
VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets
a new state of the art on VoxLingua107 language identification. Moreover, we
show that with sufficient model size, cross-lingual pretraining can outperform
English-only pretraining when translating English speech into other languages,
a setting which favors monolingual pretraining. We hope XLS-R can help to
improve speech processing tasks for many more languages of the world.

本文介绍了 XLS-R，这是一个基于 Wav2vec 2.0 的用于跨语言语音表示学习的大规模模型，其在 128 种语言的近半百万小时的公开语音音频数据上进行训练，并取得了广泛的评估表现。我们的研究致力于提升语音处理任务在更多的世界语言上的表现。