One short film, different audio descriptions. Analysing the language of audio descriptions created by students and professionals
One short film, different audio descriptions. Analysing the language of audio descriptions created by students and professionals
Author
Matamala, Anna
Abstract
Audio description is an access service that translates visuals into words which are then received auditorily by end users. This article aims to compare audio descriptions created by professionals and by students in Spanish, in order to identify commonalities and divergences, if any. Part of the VIW corpus is used to that end. Although limited in size, the VIW corpus is the first open-access multilingual multimodal corpus of audio descriptions, including various audio descriptions of the same visual input, namely the short film “What happens while”. The analysis presented in this paper focuses on quantitative data such as the number of words, number of AD units and sentences, as well as on the distribution of word classes. Where relevant, data are compared to general language corpora so as to highlight the specificities of the language of audio description. The paper also compares the most frequent words used by both groups, and a selection of semantic classes automatically retrieved is analysed. This article provides new insights into the language of audio description and shows the research potential of the VIW corpus. Audio description is an access service that translates visuals into words which are then received auditorily by end users. This article aims to compare audio descriptions created by professionals and by students in Spanish, in order to identify commonalities and divergences, if any. Part of the VIW corpus is used to that end. Although limited in size, the VIW corpus is the first open-access multilingual multimodal corpus of audio descriptions, including various audio descriptions of the same visual input, namely the short film “What happens while”. The analysis presented in this paper focuses on quantitative data such as the number of words, number of AD units and sentences, as well as on the distribution of word classes. Where relevant, data are compared to general language corpora so as to highlight the specificities of the language of audio description. The paper also compares the most frequent words used by both groups, and a selection of semantic classes automatically retrieved is analysed. This article provides new insights into the language of audio description and shows the research potential of the VIW corpus.
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