Editing machine-generated subtitle templates: A situated subtitler training experience

Abstract

Automation technologies have altered media localisation workflows as much as practitioners’ workstations and habits. Subtitling systems and streaming services now often integrate built-in automatic speech recognition (ASR) engines, sometimes even combined with machine translation engines, to produce subtitles from audio tracks. The rise of post-editors in the audiovisual translation (AVT) sector, specifically subtitling, has been a reality for some time, thus triggering the need for up-to-date training methods and academic curricula. This article examines the uses and applications of editing practices for machine-generated timed transcriptions in subtitler training environments. A situated learning experience was designed for an international team of eight AVT trainees and three educators to edit raw machine-generated subtitles (both inter- and intra-lingually) for educational videos. The publication of an accessible video book by a publishing house was the ultimate objective of this project, undertaken by an international team of English- and Spanish-speaking postgraduate students and graduates. The feedback collated after this experience through an online questionnaire proved paramount to understanding the use of subtitle post-editing for ASR-produced templates in AVT education. Interestingly, most respondents believed that subtitle post-editing training, be it intralingual or interlingual, should be further embedded in translation curricula while also identifying bottlenecks that AVT educators may find useful when developing activities of this nature.