  Using end-to-end models for speech translation (ST) has increasingly been the
focus of the ST community. These models condense the previously cascaded
systems by directly converting sound waves into translated text. However,
cascaded models have the advantage of including automatic speech recognition
output, useful for a variety of practical ST systems that often display
transcripts to the user alongside the translations. To bridge this gap, recent
work has shown initial progress into the feasibility for end-to-end models to
produce both of these outputs. However, all previous work has only looked at
this problem from the consecutive perspective, leaving uncertainty on whether
these approaches are effective in the more challenging streaming setting. We
develop an end-to-end streaming ST model based on a re-translation approach and
compare against standard cascading approaches. We also introduce a novel
inference method for the joint case, interleaving both transcript and
translation in generation and removing the need to use separate decoders. Our
evaluation across a range of metrics capturing accuracy, latency, and
consistency shows that our end-to-end models are statistically similar to
cascading models, while having half the number of parameters. We also find that
both systems provide strong translation quality at low latency, keeping 99% of
consecutive quality at a lag of just under a second.
