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.

我们的论文提出了一种名为“Regularized Batched Inputs”的新型适应性和高效的策略，通过增强输入多样性来减轻输出错误，在IWSLT Simultaneous Speech Translation (SimulST)任务中进行了实验证明，我们的方法在保持离线系统不超过2 BLEU分损失的同时实现了低延迟，并在各个语言方向上取得了几个新的最先进的结果。

基于正则化批处理输入的增量解码框架优化低延迟同时语音翻译