Most of the speech translation models heavily rely on parallel data, which is hard to collect especially for low-resource languages. To tackle this issue, we propose to build a cascaded speech translation system without leveraging any kind of paired data. We use fully unpaired data to train our unsupervised systems and evaluate our results on CoVoST 2 and CVSS. The results show that our work is comparable with some other early supervised methods in some language pairs. While cascaded systems always suffer from severe error propagation problems, we proposed denoising back-translation (DBT), a novel approach to building robust unsupervised neural machine translation (UNMT). DBT successfully increases the BLEU score by 0.7--0.9 in all three translation directions. Moreover, we simplified the pipeline of our cascaded system to reduce inference latency and conducted a comprehensive analysis of every part of our work. We also demonstrate our unsupervised speech translation results on the established website.

我们提出了一种基于叠层系统的语音翻译方法，使用全非配对数据训练了我们的无监督系统，并采用去噪回译法提高翻译结果。研究结果显示，我们的方法可以有效解决低资源语言数据难以获取的问题，并在某些语言对上表现出可比较的效果。

使用去噪反向翻译提升级联无监督语音翻译