Simultaneous speech translation (SST) aims to provide real-time translation
of spoken language, even before the speaker finishes their sentence.
Traditionally, SST has been addressed primarily by cascaded systems that
decompose the task into subtasks, including speech recognition, segmentation,
and machine translation. However, the advent of deep learning has sparked
significant interest in end-to-end (E2E) systems. Nevertheless, a major
limitation of most approaches to E2E SST reported in the current literature is
that they assume that the source speech is pre-segmented into sentences, which
is a significant obstacle for practical, real-world applications. This thesis
proposal addresses end-to-end simultaneous speech translation, particularly in
the long-form setting, i.e., without pre-segmentation. We present a survey of
the latest advancements in E2E SST, assess the primary obstacles in SST and its
relevance to long-form scenarios, and suggest approaches to tackle these
challenges.

同时语音翻译 (SST) 的目标是提供实时的口语翻译，即使在发言者完成句子之前。传统上，SST 主要通过级联系统来解决，将任务分解为子任务，包括语音识别、分割和机器翻译。然而，深度学习的出现激发了对端到端 (E2E) 系统的极大兴趣。然而，当前文献中大多数 E2E SST 方法的主要限制是它们假设源语音被预分割为句子，这对于实际的现实应用是一个重要的障碍。本论文提出了端到端的同时语音翻译，特别是在长篇设定中，即不进行预分割。我们对 E2E SST 的最新进展进行了调研，评估了 SST 的主要障碍以及其与长篇情景的相关性，并提出了应对这些挑战的方法。