In recent years, cloud-based environments have radically altered traditional subti-tling workflows that can now be conducted online by virtual teams of profession-als working together from all corners of the Earth. Moreover, automatic speech recognition (ASR) and machine translation (MT) tools are being integrated into audiovisual translation workflows. As a result, new professional profiles are emerging, but the new opportunities offered by technological progress are accom-panied by significant challenges. The present chapter illustrates the main results of ¡Sub!: Localisation Workflows that Work (2020-2021) and its follow-up ¡Sub!2 (2021-2022), two international pilot projects carried out at UNINT (Italy). A series of experiments was conducted on the OOONA cloud subtitling platform to com-pare three workflows which differed in their degree of automation. 24 subjects were recruited among postgraduate students and recent translation graduates from UNINT and Roehampton University. There were three subtitling teams working from English into Italian and three teams working from Spanish into Ital-ian, and each team comprised a Project Manager, a Spotter, a Translator and a Reviser. The teams were required to subtitle three 10-minute clips from a science documentary according to the instructions provided for each workflow. As well as submitting the target language subtitles for each clip, the teams were required to document all the steps of the process via a team logbook, screen recordings of work sessions, Quality Assessment forms and workflow reports. In addition, pre- and post-experiment questionnaires were administered to participants. All the da-ta thus collected has been analysed from a quantitative and qualitative point of view to determine the most efficient workflow, i.e., the one that ensures the best quality output in the tightest turnaround time. The results of the two pilot studies will be used to inform translator training practices, to ensure they are in line with constantly evolving market demands.
Integrating ASR and MT tools into cloud sSubtitling Workflows: the ¡Sub! and ¡Sub!2 projects
Annalisa Sandrelli
2024-01-01
Abstract
In recent years, cloud-based environments have radically altered traditional subti-tling workflows that can now be conducted online by virtual teams of profession-als working together from all corners of the Earth. Moreover, automatic speech recognition (ASR) and machine translation (MT) tools are being integrated into audiovisual translation workflows. As a result, new professional profiles are emerging, but the new opportunities offered by technological progress are accom-panied by significant challenges. The present chapter illustrates the main results of ¡Sub!: Localisation Workflows that Work (2020-2021) and its follow-up ¡Sub!2 (2021-2022), two international pilot projects carried out at UNINT (Italy). A series of experiments was conducted on the OOONA cloud subtitling platform to com-pare three workflows which differed in their degree of automation. 24 subjects were recruited among postgraduate students and recent translation graduates from UNINT and Roehampton University. There were three subtitling teams working from English into Italian and three teams working from Spanish into Ital-ian, and each team comprised a Project Manager, a Spotter, a Translator and a Reviser. The teams were required to subtitle three 10-minute clips from a science documentary according to the instructions provided for each workflow. As well as submitting the target language subtitles for each clip, the teams were required to document all the steps of the process via a team logbook, screen recordings of work sessions, Quality Assessment forms and workflow reports. In addition, pre- and post-experiment questionnaires were administered to participants. All the da-ta thus collected has been analysed from a quantitative and qualitative point of view to determine the most efficient workflow, i.e., the one that ensures the best quality output in the tightest turnaround time. The results of the two pilot studies will be used to inform translator training practices, to ensure they are in line with constantly evolving market demands.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
