标题： 何时发言的学习：离线模型同时语音到语音翻译中的延迟与质量权衡 显示英文标题

标题： Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models

摘要： 语音到语音翻译（S2ST）的近期工作主要集中在离线设置上，其中完整的输入语句在生成任何输出之前即可获得。 然而，在许多现实场景中，这并不合理。 在对延迟敏感的应用程序中，与其等待整个语句，不如在输入信息可用时立即提供翻译。 在这项工作中，我们介绍了一个面向实际应用场景的同步S2ST系统。 我们的系统支持从57种语言翻译成英语，并具有可调参数以动态调整输出的延迟——包括四种用于确定何时输出序列的策略。 我们展示了这些策略在延迟仅略有增加的情况下实现了与Greedy（等待-$k$）基线相当的离线水平准确性。 我们开源了我们的评估代码和交互式测试脚本，以促进未来同步S2ST研究和应用开发。 显示英文摘要

摘要： Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however, is not reasonable in many real-world scenarios. In latency-sensitive applications, rather than waiting for the full utterance, translations should be spoken as soon as the information in the input is present. In this work, we introduce a system for simultaneous S2ST targeting real-world use cases. Our system supports translation from 57 languages to English with tunable parameters for dynamically adjusting the latency of the output -- including four policies for determining when to speak an output sequence. We show that these policies achieve offline-level accuracy with minimal increases in latency over a Greedy (wait-$k$) baseline. We open-source our evaluation code and interactive test script to aid future SimulS2ST research and application development.