Improving Cascaded Unsupervised Speech Translation with Denoising
  Back-translation
Yu-Kuan Fu Liang-Hsuan Tseng Jiatong Shi Chen-An Li Tsu-Yuan Hsu Shinji Watanabe Hung-yi Lee
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.