thesis

Short-term accommodation of Hong Kong English speakers towards native English accents and the effect of language attitudes

Abstract

Accommodation, also known as convergence, refers to a process whereby a speaker changes the way he or she speaks to be more similar to another speaker. This dissertation focuses on two themes: language attitudes and short-term accommodation. A study using the matched-guise method is conducted to examine Hong Kong people’s attitudes towards British English, American English and Hong Kong English (henceforth HKE). Results suggest that after the handover British English is still rated as the most prestigious English variety in Hong Kong. HKE is also found to have a high level of acceptance in terms of social attractiveness. For short-term accommodation, two studies are conducted to investigate the phonetic convergence of HKE speakers towards native English accents, and the effect of language attitudes on convergence. Study 2 consists of a group of HKE speakers completing separate map tasks with a Received Pronunciation speaker and a General American English speaker. Their pronunciations of the THOUGHT vowel, the PATH vowel, rhoticity, fricative /z/ and fricative /θ/ are examined before, during and after the map tasks. The results suggest that the HKE speakers produce more fricative [z] and converge on rhoticity after exposure to the native accents. However, divergence is found on the PATH vowel and fricative /θ/, and maintenance is found on the THOUGHT vowel. These findings suggest that the HKE speakers tend to converge on the linguistic features which are more salient to them. Study 3 examines the effect of language attitudes on speech convergence, and no correlation is found between language attitudes and the HKE speakers’ convergence on rhoticity. Finally, the hybrid exemplar-based model is proposed to explain the complex results of the three studies. It provides a framework for speech accommodation which covers speech perception and production, and includes social factors as important elements in the model

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