  Speech translation for subtitling (SubST) is the task of automatically
translating speech data into well-formed subtitles by inserting subtitle breaks
compliant to specific displaying guidelines. Similar to speech translation
(ST), model training requires parallel data comprising audio inputs paired with
their textual translations. In SubST, however, the text has to be also
annotated with subtitle breaks. So far, this requirement has represented a
bottleneck for system development, as confirmed by the dearth of publicly
available SubST corpora. To fill this gap, we propose a method to convert
existing ST corpora into SubST resources without human intervention. We build a
segmenter model that automatically segments texts into proper subtitles by
exploiting audio and text in a multimodal fashion, achieving high segmentation
quality in zero-shot conditions. Comparative experiments with SubST systems
respectively trained on manual and automatic segmentations result in similar
performance, showing the effectiveness of our approach.
