  The rapid development of single-modal pre-training has prompted researchers
to pay more attention to cross-modal pre-training methods. In this paper, we
propose a unified-modal speech-unit-text pre-training model, SpeechUT, to
connect the representations of a speech encoder and a text decoder with a
shared unit encoder. Leveraging hidden-unit as an interface to align speech and
text, we can decompose the speech-to-text model into a speech-to-unit model and
a unit-to-text model, which can be jointly pre-trained with unpaired speech and
text data respectively. Our proposed SpeechUT is fine-tuned and evaluated on
automatic speech recognition (ASR) and speech translation (ST) tasks.
Experimental results show that SpeechUT gets substantial improvements over
strong baselines, and achieves state-of-the-art performance on both the
LibriSpeech ASR and MuST-C ST tasks. To better understand the proposed
SpeechUT, detailed analyses are conducted. The code and pre-trained models are
available at https://aka.ms/SpeechUT.
