Automatic subtitling is the task of automatically translating the speech of
an audiovisual product into short pieces of timed text, in other words,
subtitles and their corresponding timestamps. The generated subtitles need to
conform to multiple space and time requirements (length, reading speed) while
being synchronised with the speech and segmented in a way that facilitates
comprehension. Given its considerable complexity, automatic subtitling has so
far been addressed through a pipeline of elements that deal separately with
transcribing, translating, segmenting into subtitles and predicting timestamps.
In this paper, we propose the first direct automatic subtitling model that
generates target language subtitles and their timestamps from the source speech
in a single solution. Comparisons with state-of-the-art cascaded models trained
with both in- and out-domain data show that our system provides high-quality
subtitles while also being competitive in terms of conformity, with all the
advantages of maintaining a single model.

本论文提出了一种直接自动生成目标语言字幕及相应时间戳的自动字幕模型，与现有的级联模型相比，在提供高质量字幕的同时也具有更高的一致性和维护单一模型的优点。