  Simultaneous speech-to-text translation is widely useful in many scenarios.
The conventional cascaded approach uses a pipeline of streaming ASR followed by
simultaneous MT, but suffers from error propagation and extra latency. To
alleviate these issues, recent efforts attempt to directly translate the source
speech into target text simultaneously, but this is much harder due to the
combination of two separate tasks. We instead propose a new paradigm with the
advantages of both cascaded and end-to-end approaches. The key idea is to use
two separate, but synchronized, decoders on streaming ASR and direct
speech-to-text translation (ST), respectively, and the intermediate results of
ASR guide the decoding policy of (but is not fed as input to) ST. During
training time, we use multitask learning to jointly learn these two tasks with
a shared encoder. En-to-De and En-to-Es experiments on the MuSTC dataset
demonstrate that our proposed technique achieves substantially better
translation quality at similar levels of latency.
