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 Spanishspeaking 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.