Effort in Semi-Automatized Subtitling Processes

Abstract

The presented study investigates the impact of automatic speech recognition (ASR) and assisting scripts on effort during transcription and translation processes, two main subprocesses of interlingual subtitling. Applying keylogging and eye tracking, this study takes a first look at how the integration of ASR impacts these subprocesses. 12 professional subtitlers and 13 translation students were recorded performing two intralingual transcriptions and three translation tasks to evaluate the impact on temporal, technical, and cognitive effort, and split-attention. Measures include editing time, visit count and duration, insertions, and deletions. The main findings show that, in both tasks, ASR did not significantly impact task duration, but participants had fewer keystrokes, indicating less technical effort. Regarding visual attention the existence of an ASR script did not decrease the time spent replaying the video. The study also shows that students were less efficient in their typing and made more use of the ASR script. The results are discussed in context of the experiment and an outlook on further research is given.

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

“…There seem to have been fewer efforts devoted to ASR in interlingual (media) translation, with only a few studies on the post-editing of ASR-generated content, such as Matamala et al (2015), Tardel (2020), andKoponen (2021). Largescale projects on the use of ASR technologies in AVT include TransLectures (Transcription andTranslation of Video Lectures, 2011-2014) [2018][2019][2020][2021].…”