Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?
This work addresses the need for more efficient subtitling processes in media dissemination, though it appears incremental by comparing existing methods without major breakthroughs.
The paper tackled the problem of automating subtitling for audiovisual content by exploring speech translation methods, finding that direct end-to-end and cascade approaches can improve subtitle conformity to timing and segmentation constraints, but length alone is insufficient for optimal results.
Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily. Although Neural Machine Translation (NMT) can speed up the process of translating audiovisual content, large manual effort is still required for transcribing the source language, and for spotting and segmenting the text into proper subtitles. Creating proper subtitles in terms of timing and segmentation highly depends on information present in the audio (utterance duration, natural pauses). In this work, we explore two methods for applying Speech Translation (ST) to subtitling: a) a direct end-to-end and b) a classical cascade approach. We discuss the benefit of having access to the source language speech for improving the conformity of the generated subtitles to the spatial and temporal subtitling constraints and show that length is not the answer to everything in the case of subtitling-oriented ST.