Simultaneous text translation and end-to-end speech translation have recently
made great progress but little work has combined these tasks together. We
investigate how to adapt simultaneous text translation methods such as wait-k
and monotonic multihead attention to end-to-end simultaneous speech translation
by introducing a pre-decision module. A detailed analysis is provided on the
latency-quality trade-offs of combining fixed and flexible pre-decision with
fixed and flexible policies. We also design a novel computation-aware latency
metric, adapted from Average Lagging.

本文调查了如何将同步文本翻译方法与同时语音翻译相结合，并通过引入预决定模块来适应于端到端的同时语音翻译。同时，对将固定和灵活的预决策与固定和灵活的策略相结合的延迟质量权衡进行了详细分析。作者还设计了一种新的计算感知的延迟度量方法，该方法从平均滞后进行调整。

从 SimulMT 到 SimulST: 将同时翻译文本转换为端到端同时翻译语音

SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End  Simultaneous Speech Translation

Previous work on end-to-end translation from speech has primarily used
frame-level features as speech representations, which creates longer, sparser
sequences than text. We show that a naive method to create compressed
phoneme-like speech representations is far more effective and efficient for
translation than traditional frame-level speech features. Specifically, we
generate phoneme labels for speech frames and average consecutive frames with
the same label to create shorter, higher-level source sequences for
translation. We see improvements of up to 5 BLEU on both our high and low
resource language pairs, with a reduction in training time of 60%. Our
improvements hold across multiple data sizes and two language pairs.

研究采用类音素语音表示替代传统帧级语音特征作为源输入，在端到端的语音翻译任务中，相比传统方法，模型性能有显著提高，同时训练时间减少了 60%。