  Applications designed for simultaneous speech translation during events such
as conferences or meetings need to balance quality and lag while displaying
translated text to deliver a good user experience. One common approach to
building online spoken language translation systems is by leveraging models
built for offline speech translation. Based on a technique to adapt end-to-end
monolingual models, we investigate multilingual models and different
architectures (end-to-end and cascade) on the ability to perform online speech
translation. On the multilingual TEDx corpus, we show that the approach
generalizes to different architectures. We see similar gains in latency
reduction (40% relative) across languages and architectures. However, the
end-to-end architecture leads to smaller translation quality losses after
adapting to the online model. Furthermore, the approach even scales to
zero-shot directions.
