  Speech segmentation, which splits long speech into short segments, is
essential for speech translation (ST). Popular VAD tools like WebRTC VAD have
generally relied on pause-based segmentation. Unfortunately, pauses in speech
do not necessarily match sentence boundaries, and sentences can be connected by
a very short pause that is difficult to detect by VAD. In this study, we
propose a speech segmentation method using a binary classification model
trained using a segmented bilingual speech corpus. We also propose a hybrid
method that combines VAD and the above speech segmentation method. Experimental
results revealed that the proposed method is more suitable for cascade and
end-to-end ST systems than conventional segmentation methods. The hybrid
approach further improved the translation performance.
