  The amount of labeled data to train models for speech tasks is limited for
most languages, however, the data scarcity is exacerbated for speech
translation which requires labeled data covering two different languages. To
address this issue, we study a simple and effective approach to build speech
translation systems without labeled data by leveraging recent advances in
unsupervised speech recognition, machine translation and speech synthesis,
either in a pipeline approach, or to generate pseudo-labels for training
end-to-end speech translation models. Furthermore, we present an unsupervised
domain adaptation technique for pre-trained speech models which improves the
performance of downstream unsupervised speech recognition, especially for
low-resource settings. Experiments show that unsupervised speech-to-text
translation outperforms the previous unsupervised state of the art by 3.2 BLEU
on the Libri-Trans benchmark, on CoVoST 2, our best systems outperform the best
supervised end-to-end models (without pre-training) from only two years ago by
an average of 5.0 BLEU over five X-En directions. We also report competitive
results on MuST-C and CVSS benchmarks.
