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Robustness of phonolexical representations relates to phonetic flexibility for difficult second language sound contrasts

Published online by Cambridge University Press:  06 September 2018

MIQUEL LLOMPART  and
EVA REINISCH
MIQUEL LLOMPART*
Affiliation:
Ludwig Maximilian University Munich
EVA REINISCH
Affiliation:
Ludwig Maximilian University Munich
*
Address for correspondence: Miquel Llompart, Institute of Phonetics and Speech Processing, Ludwig Maximilian University Munich, Schellingstraße 3, 80799 Munich, Germany. M.Llompart@phonetik.uni-muenchen.de
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Abstract

Listening to speech entails adapting to vast amounts of variability in the signal. The present study examined the relationship between flexibility for adaptation in a second language (L2) and robustness of L2 phonolexical representations. Phonolexical encoding and phonetic flexibility for German learners of English were assessed by means of a lexical decision task containing nonwords with sound substitutions and a distributional learning task, respectively. Performance was analyzed for an easy (/i/-/ɪ/) and a difficult contrast (/ε/-/æ/, where /æ/ does not exist in German). Results showed that for /i/-/ɪ/ listeners were quite accurate in lexical decision, and distributional learning consistently triggered shifts in categorization. For /ε/-/æ/, lexical decision performance was poor but individual participants’ scores related to performance in distributional learning: the better learners were in their lexical decision, the smaller their categorization shift. This suggests that, for difficult L2 contrasts, rigidity at the phonetic level relates to better lexical performance.

Keywords

second language learningspeech perceptionlexical representationsphoneticsflexibility
Type
Research Article
Information
Bilingualism: Language and Cognition , Volume 22 , Issue 5 , November 2019 , pp. 1085 - 1100
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

This project was funded by a grant from the German Research Foundation (DFG; grant nr. RE 3047/1-1) to the second author. This work is part of the first author's Ph.D. project. Parts of this work were presented at the 23rd Annual Conference on Architectures and Mechanisms for Language Processing, 2017, in Lancaster, UK. We would like to thank Rosa Franzke for her help with testing participants and Christopher Carignan for comments on a previous version of the manuscript.

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