  The success of end-to-end speech-to-text translation (ST) is often achieved
by utilizing source transcripts, e.g., by pre-training with automatic speech
recognition (ASR) and machine translation (MT) tasks, or by introducing
additional ASR and MT data. Unfortunately, transcripts are only sometimes
available since numerous unwritten languages exist worldwide. In this paper, we
aim to utilize large amounts of target-side monolingual data to enhance ST
without transcripts. Motivated by the remarkable success of back translation in
MT, we develop a back translation algorithm for ST (BT4ST) to synthesize pseudo
ST data from monolingual target data. To ease the challenges posed by
short-to-long generation and one-to-many mapping, we introduce self-supervised
discrete units and achieve back translation by cascading a target-to-unit model
and a unit-to-speech model. With our synthetic ST data, we achieve an average
boost of 2.3 BLEU on MuST-C En-De, En-Fr, and En-Es datasets. More experiments
show that our method is especially effective in low-resource scenarios.
