In this paper, we describe our submission to the Simultaneous Speech
Translation at IWSLT 2022. We explore strategies to utilize an offline model in
a simultaneous setting without the need to modify the original model. In our
experiments, we show that our onlinization algorithm is almost on par with the
offline setting while being $3\times$ faster than offline in terms of latency
on the test set. We also show that the onlinized offline model outperforms the
best IWSLT2021 simultaneous system in medium and high latency regimes and is
almost on par in the low latency regime. We make our system publicly available.

本文介绍了我们在 IWSLT 2022 Simultaneous Speech Translation 比赛中的参赛作品，探讨了如何在不修改原始模型的情况下，在同步设置中利用离线模型的策略。在实验中，我们展示了我们的在线算法几乎与离线设置相等，而在测试集上的延迟方面比离线快 3 倍。我们还展示了在线化的离线模型在中高延迟区间比最佳 IWSLT2021 同声翻译系统表现更好，在低延迟区间则几乎持平。我们的系统已经公开发布。