  In simultaneous speech translation (SimulST), finding the best trade-off
between high translation quality and low latency is a challenging task. To meet
the latency constraints posed by the different application scenarios, multiple
dedicated SimulST models are usually trained and maintained, generating high
computational costs. In this paper, motivated by the increased social and
environmental impact caused by these costs, we investigate whether a single
model trained offline can serve not only the offline but also the simultaneous
task without the need for any additional training or adaptation. Experiments on
en->{de, es} indicate that, aside from facilitating the adoption of
well-established offline techniques and architectures without affecting
latency, the offline solution achieves similar or better translation quality
compared to the same model trained in simultaneous settings, as well as being
competitive with the SimulST state of the art.

Computation and Language