Martín, AlejandroGonzález Carrasco, IsraelRodríguez Fernández, VíctorSouto Rico, Mónica MaríaCamacho, DavidRuiz Mezcua, María Belén2021-12-032022-02-082021-02-08Martín, A., González-Carrasco, I., Rodriguez-Fernandez, V. et al. Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05751-y0941-0643https://hdl.handle.net/10016/33720Subtitles are a key element to make any media content accessible for people who suffer from hearing impairment and for elderly people, but also useful when watching TV in a noisy environment or learning new languages. Most of the time, subtitles are generated manually in advance, building a verbatim and synchronised transcription of the audio. However, in TV live broadcasts, captions are created in real time by a re-speaker with the help of a voice recognition software, which inevitability leads to delays and lack of synchronisation. In this paper, we present Deep-Sync, a tool for the alignment of subtitles with the audio-visual content. The architecture integrates a deep language representation model and a real-time voice recognition software to build a semantic-aware alignment tool that successfully aligns most of the subtitles even when there is no direct correspondence between the re-speaker and the audio content. In order to avoid any kind of censorship, Deep-Sync can be deployed directly on users' TVs causing a small delay to perform the alignment, but avoiding to delay the signal at the broadcaster station. Deep-Sync was compared with other subtitles alignment tool, showing that our proposal is able to improve the synchronisation in all tested cases.eng© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Naturetv broadcastingsynchronisationlanguage modeldeep neural networksmachine learningresearch articleInformáticahttps://doi.org/10.1007/s00521-021-05751-yopen access115NEURAL COMPUTING & APPLICATIONSAR/0000028719