  User studies have shown that reducing the latency of our simultaneous lecture
translation system should be the most important goal. We therefore have worked
on several techniques for reducing the latency for both components, the
automatic speech recognition and the speech translation module. Since the
commonly used commitment latency is not appropriate in our case of continuous
stream decoding, we focused on word latency. We used it to analyze the
performance of our current system and to identify opportunities for
improvements. In order to minimize the latency we combined run-on decoding with
a technique for identifying stable partial hypotheses when stream decoding and
a protocol for dynamic output update that allows to revise the most recent
parts of the transcription. This combination reduces the latency at word level,
where the words are final and will never be updated again in the future, from
18.1s to 1.1s without sacrificing performance in terms of word error rate.
