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Patterns of growing standardisation and interference in interpreted German discourse

This study compares simultaneously interpreted German speech to non-interpreted German discourse in order to determine whether interpreted language is characterised by any of the laws that have been found to feature in translated text, i.e. the law of growing standardisation and the law of interference. It is hypothesised that interpreters typically exaggerate German communicative norms, thereby producing manifestations of growing standardisation. In order to test this hypothesis, comparative and parallel analyses are carried out using corpora of interpreted and non-interpreted discourse. During the comparative phase, two types of interpreted German speech are each compared to non-interpreted language and to each other in order to determine how interpreted speech differs from non-interpreted discourse. During the parallel analysis, the interpreted German segments are compared to their source language counterparts with the aim of determining the reasons for the production of the patterns discovered during the first phase. The results indicate that interpreters do not produce patterns similar to those that characterise translated text: neither the law of growing standardisation nor the law of interference is manifest in the data. Instead, a different feature, namely an increased degree of generalisation, is discovered in the interpreters‟ output. This feature appears to be the result of the use of strategies that enable interpreters to deal with time, memory and linearity constraints inherent in SI. It can hence be confirmed that interpreted German differs from non-interpreted German discourse in certain respects. / Linguistics / M.A. (Linguistics)

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:umkn-dsp01.int.unisa.ac.za:10500/4710
Date30 November 2010
CreatorsDose, Stephanie
ContributorsWallmach, A.K.
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeDissertation
Format1 online resource (181 leaves)

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