  For real-life applications, it is crucial that end-to-end spoken language
translation models perform well on continuous audio, without relying on
human-supplied segmentation. For online spoken language translation, where
models need to start translating before the full utterance is spoken, most
previous work has ignored the segmentation problem. In this paper, we compare
various methods for improving models' robustness towards segmentation errors
and different segmentation strategies in both offline and online settings and
report results on translation quality, flicker and delay. Our findings on five
different language pairs show that a simple fixed-window audio segmentation can
perform surprisingly well given the right conditions.
