End-to-end speech-to-text translation can provide a simpler and smaller system but is facing the challenge of data scarcity. Pre-training methods can leverage unlabeled data and have been shown to be effective on data-scarce settings. In this work, we explore whether self-supervised pre-trained speech representations can benefit the speech translation task in both high- and low-resource settings, whether they can transfer well to other languages, and whether they can be effectively combined with other common methods that help improve low-resource end-to-end speech translation such as using a pre-trained high-resource speech recognition system. We demonstrate that self-supervised pre-trained features can consistently improve the translation performance, and cross-lingual transfer allows to extend to a variety of languages without or with little tuning.

本文旨在探索自监督预训练语音表示是否可以优化高和低资源语音翻译任务，并且这些语音表示是否可以有效地和其他常见的低资源端到端语音翻译方法相结合，以及这些表示是否可以良好地跨语种迁移。结果表明，自监督预训练功能可以始终提高翻译性能，并且跨语言传输可以扩展到各种语言而无需或只需要少量调整。

自我监督表示改善端到端语音翻译