Assessing quality in live interlingual subtitling: a new challenge

Abstract

Quality-assessment models for live interlingual subtitling are virtually non-existent. In this study we investigate whether and to what extent existing models from related translation modes, more specifically the Named Entity Recognition (NER) model for intralingual live subtitling, provide a good starting point. Having conducted a survey of the major quality parameters in different forms of subtitling, we proceed to adapt this model. The model measures live intralingual quality on the basis of different types of recognition error by the speech-recognition software, and edition errors by the respeaker, with reference to their impact on the viewer’s comprehension. To test the adapted model we conducted a context-based study comprising the observation of the live interlingual subtitling process of four episodes of Dansdate, broadcast by the Flemish commercial broadcaster VTM in 2015. The process observed involved four “subtitlers”: the respeaker/interpreter, a corrector, a speech-to-text interpreter and a broadcaster, all of whom performed different functions. The data collected allow errors in the final product and in the intermediate stages to be identified: they include when and by whom they were made. The results show that the NER model can be applied to live interlingual subtitling if it is adapted to deal with errors specific to translation proper.

Measuring quality in translation for dubbing: a quality assessment model proposal for trainers and stakeholders

“…Whether quality can be quantified in the first place can be debatable, of course, since it necessarily implies a certain degree of subjectivity. There have been similar initiatives to address the issue of quality in interlingual subtitling (Nikolić, 2021, Pedersen, 2017Robert & Remael, 2016), intralingual live subtitling (Romero-Fresco & Martínez Pérez, 2015) and interlingual live subtitling (Robert & Remael, 2017;Romero-Fresco & Pöchhacker, 2017). Among the existing models, the FAR model (Pedersen, 2017) proposes an error-based assessment method based on: Functional equivalence (semantics, style), Acceptability (grammar, spelling, idiomaticity), and Readability (segmentation and spotting, reading speed, line length punctuation, use of italics).…”

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Quality assessment in the field of Audiovisual Translation (AVT) has been addressed by several scholars, particularly in relation to interlingual subtitling (Pedersen, 2017;Robert & Remael, 2016), intralingual live subtitling (Romero-Fresco & Martínez Pérez, 2015) and interlingual live subtitling (Robert & Remael, 2017;Romero-Fresco & Pöchhacker, 2017), but to-date no model in relation to dubbing has been proposed. As with other AVT modes, the need for a quality assessment method in dubbing arises in academic and in-house training contexts.

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Examining Students’ Translation Quality in Subtitling for the Deaf and Hard of Hearing

“…Some studies have already attempted to explore the equivalence of the subtitling arena. Still, most studies in the field of subtitling have only focused on addressing "normal audiences," not particular audiences and their translation technique (Ohene-Djan, Wright & Smith, 2007;Pedersen, 2017;Robert & Remael, 2017;Hudi, Hartono & Yulisari, 2020;Budiana, Sutopo & Rukmini, 2017;Supardi & Putri, 2018, Aminudin & Hidayati, 2021. Pedersen (2017) assessed the quality of subtitling in terms of functional equivalence, acceptability, and readability.…”