By Jorge Díaz Cintas, Rocío Baños Piñero
This publication bargains an up to date survey of the current situation in Audiovisual Translation, delivering a thought-provoking account of a few of the main consultant parts at present being researched during this box around the globe. The booklet discusses theoretical concerns and offers worthwhile and functional insights into specialist practices.
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Extra resources for Audiovisual Translation in a Global Context: Mapping an Ever-changing Landscape
An emphasis will be placed on respoken subtitles, the type in most common use nowadays. 1 Following an introduction outlining the basic requirements that such a model might be expected to fulfil, an overview of the traditional methods used in what is known as word error rate (WER) is given. This is then followed by examples of the different types of error assessed by the NER model in English, Spanish, Italian and German with, finally, an explanation of the application of the NER model to real-life subtitles.
In this sense, the main platforms include: ad-hoc television channels (such as the European Union’s ‘Europe by Satellite’ and C-SPAN in the United States), dedicated portals and websites with live and archived material (such as the European Parliament’s ‘EuroparlTV’) and social media (the UN webcast, which can be accessed at http://webtv. org, as well as on Facebook, YouTube and Twitter). Audiovisual and multimedia platforms provide an attractive, immediate, user-friendly, readily updatable way of delivering information.
1 Main components of the model Good quality live subtitles may be expected to reach a 98 per cent accuracy rate using the following model: Accuracy N – E – R × 100 N CE (correct editings): Assessment: N: Number of words in the respoken subtitles, including commands (punctuation marks, speaker identification). E: Editing errors, usually caused by strategies applied by the respeaker. In other words, these errors are the result of a judgement or decision on the part of the respeaker. g. 3 The respeaker Accuracy Rate in Live Subtitling: The NER Model 33 sometimes also adds idea units or paraphrases the original text, losing information or introducing wrong information, perhaps due to a misunderstanding of the original text.
Audiovisual Translation in a Global Context: Mapping an Ever-changing Landscape by Jorge Díaz Cintas, Rocío Baños Piñero