  The audio segmentation mismatch between training data and those seen at
run-time is a major problem in direct speech translation. Indeed, while systems
are usually trained on manually segmented corpora, in real use cases they are
often presented with continuous audio requiring automatic (and sub-optimal)
segmentation. After comparing existing techniques (VAD-based, fixed-length and
hybrid segmentation methods), in this paper we propose enhanced hybrid
solutions to produce better results without sacrificing latency. Through
experiments on different domains and language pairs, we show that our methods
outperform all the other techniques, reducing by at least 30% the gap between
the traditional VAD-based approach and optimal manual segmentation.
