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Article published In:
Audiovisual translation in context: Granting access to digital mediascapes
Edited by Jorge Díaz-Cintas, Alessandra Rizzo and Cinzia Giacinta Spinzi
[Translation and Translanguaging in Multilingual Contexts 9:3] 2023
► pp. 298–315

¡Sub! localisation workflows (th)at work

| Roehampton University
| Università degli Studi Internazionali di Roma (UNINT)
Over the last few years, cloud-based environments have simplified traditional localisation workflows and have made it possible for virtual teams of audiovisual translation (AVT) professionals to work together from all corners of the earth (Díaz Cintas and Massidda 2019). In addition, AI-powered technologies have been integrated into localisation workflows to accelerate translation processes: this has led to a progressive automation of AVT practices and has created brand new roles for language professionals. This paper presents the preliminary results of the international pilot project ¡Sub! Localisation Workflows (th)at Work (2020–2022). A series of experiments was conducted in the spring of 2021 to compare three different workflows in the subtitling of documentaries: traditional (i.e., using only subtitling software), semi-automated (using automatic speech recognition and captioning) and fully automated (relying on automatic speech recognition, captioning and machine translation). The experiments involved twenty-four final-year MA students and recent graduates from UNINT and Roehampton University (twelve of them working from English into Italian and twelve from Spanish into Italian), in subtitling teams that included a project manager, a spotter, a subtitler, and a reviser. All the work was recorded via screencast technology and documented in a project logbook, a quality assessment form, and a workflow summary sheet. The aim of the experiments was to identify the most effective workflow equation, i.e., the one able to deliver the best quality output in the tightest turnaround time. This paper illustrates the experimental set-up and materials and discusses the preliminary results emerging from a quantitative and qualitative analysis of our data.
Keywords: audiovisual translation, subtitling, automatic speech recognition, machine translation
Article outline
  • 1.Introduction
  • 2.State-of-the-art of the entertainment and media industries
  • 3.New trends in AVT technology
  • 4.¡Sub! localisation workflows (th)at work
    • 4.1Objectives
    • 4.2Methodology: Subjects, tools, and materials
  • 5.Data analysis
    • 5.1Quantitative analysis
    • 5.2Qualitative analysis
  • 6.Discussion and conclusions
  • Acknowledgements
  • Note
  • References
Published online: 9 November 2023
DOI logo
https://doi.org/10.1075/ttmc.00115.mas
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Cited by (2)

Cited by two other publications

Sandrelli, Annalisa
2024. Integrating ASR and MT Tools into Cloud Subtitling Workflows: The ¡Sub! and ¡Sub!2 Projects. In New Advances in Translation Technology [New Frontiers in Translation Studies, ], ► pp. 79 ff. DOI logo
Massidda, Serenella
2023. Disruptive AVT workflows in the age of streaming. Target. International Journal of Translation Studies 35:3 ► pp. 455 ff. DOI logo

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