Into the language of museum audio descriptions: a corpus-based study

ABSTRACT

The paper portrays a linguistic and textual analysis of a corpus of 18 museum audio descriptions (ADs) (a fairly neglected area of accessibility and audiovisual translation research) in order to pinpoint the salient features of this text type and relate them to current AD literature and guidelines. Results show that scripted and recorded museum AD texts comply with recommendations only partially. They guarantee vivid, imaginative and diverse language as well as substantial text informativity through the combination of high lexical diversity and the extensive use of descriptive adjectives as well as substantial lexical density. In spite of the use of short words, however, museum ADs seem more lexically and syntactically complex than expected, with their use of opaque technical terms, heavy adjectival phrases and long sentences. More systematic and contrastive research will help to (dis)confirm these results, whereas audience reception research will contribute to determine the real degree of usability of these new text types.

KEYWORDS:

• Audio description
• museum
• corpus
• adjectives

Acknowledgements

The information and views set out in this article are those of the author and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Elisa Perego is an associate professor at the University of Trieste. She teaches English linguistics, and the history and methodology of translation. Her research interests and publications lie mainly in the field of audiovisual translation, AVT accessibility and reception, and the use of eye tracking methodology in AVT research. Her most researched topics are subtitling and audio description, which are studied mainly from a linguistic and cognitive perspective. Elisa is currently the coordinator of the European project ADLAB PRO (2016-2019) on audio description.

Notes

1. A module of the EU project ADLAB PRO course curriculum (www.adlabpro.eu), aimed at creating free-access, flexible, didactic materials for the training of audio describers, will be devoted to the AD of static art and environment.

2. Provided by VocalEyes.

3. Brevity is known to be an asset in different contexts, e.g., it has been demonstrated that podcasts lasting no more than 10–15 minutes are more effective for learning whereas long podcasts may decrease attention and reduce comprehension (Pitt & Edwards, 2003; Deibel, 2008).

4. The audio descriptive guides at the British Museum are available free of charge for blind or partially sighted visitors, and others who find AD helpful. They contain detailed description of objects and some include curators’ commentaries, too. Audio descriptive guides are available on a touchpad device, and AD is provided for some special exhibitions as well.

5. The mission of VocalEyes is to work with blind and partially sighted people to enhance engagement with the arts through AD. It was established in 1998 to help theatre venues and producers meet the needs of blind and partially sighted audiences. It then expanded its work into other areas such as museums, galleries and heritage, architecture, contemporary dance, and AD for young people. All the work of VocalEyes is founded on solid research and consultation with experts in the field as well as with VIPs. Currently, VocalEyes is the largest organisation offering comprehensive description services nationwide, working to ensure the highest standards of delivery and promoting increased access provision.

6. Conventionally, there are two ways of calculating TTR. If the running words (tokens) are divided by the different words in the text (types), a decimal number is obtained. Another way of presenting the type / token ratio is a percentage of types for tokens. In this case we can calculate TTR as follows: (number of types/number of tokens) * 100.

7. Descriptors include colour, size/quantity/extent, time, evaluative/emotive and miscellaneous adjectives. Classifiers include relational/classificational/restrictive adjectives delimiting the referent of a noun in relation to other referents; affiliative adjectives, designating the national or religious group to which a referent belongs; topical or other types of adjectives giving the subject area or showing a relationship with a noun, e.g. peripheral adjectives including other word classes that can be used as adjectives.

8. As a frame of reference: an adult second language learner writing (and in general spoken language) would have a variety approximating around 40–70%, while a native speaker adult academic writing would typically have a measure of around 80–105% (Durán et al., 2004). The TTR found in a study on film AD was 3.85 (Arma, 2012).

Additional information

Funding

Preparation of this article was supported by The University of Trieste Research Fund FRA 2015 (Accessibility through translation: Museum audio description), and by ADLAB PRO (Audio Description: A Laboratory for the Development of a New Professional Profile), financed by the European Union under the Erasmus+ Programme, Key Action 2 – Strategic Partnerships, Project number: 2016-1-IT02-KA203-024311, both awarded to Anonymized.