  Incremental Decoding is an effective framework that enables the use of an
offline model in a simultaneous setting without modifying the original model,
making it suitable for Low-Latency Simultaneous Speech Translation. However,
this framework may introduce errors when the system outputs from incomplete
input. To reduce these output errors, several strategies such as Hold-$n$,
LA-$n$, and SP-$n$ can be employed, but the hyper-parameter $n$ needs to be
carefully selected for optimal performance. Moreover, these strategies are more
suitable for end-to-end systems than cascade systems. In our paper, we propose
a new adaptable and efficient policy named "Regularized Batched Inputs". Our
method stands out by enhancing input diversity to mitigate output errors. We
suggest particular regularization techniques for both end-to-end and cascade
systems. We conducted experiments on IWSLT Simultaneous Speech Translation
(SimulST) tasks, which demonstrate that our approach achieves low latency while
maintaining no more than 2 BLEU points loss compared to offline systems.
Furthermore, our SimulST systems attained several new state-of-the-art results
in various language directions.
