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Incorporating tone in the modelling of wordlikeness judgements

Published online by Cambridge University Press:  01 March 2021

Youngah Do Open the ORCID record for Youngah Do [Opens in a new window]  and
Ryan Ka Yau Lai Open the ORCID record for Ryan Ka Yau Lai [Opens in a new window]
Youngah Do*
Affiliation:
University of Hong Kong
Ryan Ka Yau Lai*
Affiliation:
University of California, Santa Barbara
*
*E-mail: [email protected], [email protected].
*E-mail: [email protected], [email protected].
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Abstract

Various phonotactic models have been proposed for the prediction of wordlikeness judgements, most of which have focused primarily on segments. This article aims to model wordlikeness judgements when tone is incorporated. We first show how the two major determinants of wordlikeness judgements, i.e. phonotactic probability and neighbourhood density, can be measured when tone is involved. To test the role of the two determinants of wordlikeness judgements in a tone language, judgement data were obtained from speakers of Cantonese. Bayesian modelling was then used to model the judgement data, showing that phonotactic probability, but not neighbourhood density, influences wordlikeness judgements. We also show that phonotactic probability affects the tendency to judge items as absolutely perfect or more or less wordlike, while it does not affect judgements that an item is absolutely not wordlike. Implications of these results for phonotactic modelling and processes involved in wordlikeness judgements are discussed.

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Articles
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Phonology , Volume 37 , Issue 4 , November 2020 , pp. 577 - 615
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Copyright © The Author(s), 2021. Published by Cambridge University Press

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Footnotes

The authors wish to thank Diana Archangeli, Adam Albright and the audience at Society for Computation in Linguistics (SciL) in 2019 for their valuable comments. Thanks are also due to the editors, associate editor and reviewers. The research for this paper builds on Do & Lai (forthcoming). We also wish to thank Andries Coetzee, Roger Levy and reviewers of that paper, whose feedback was important for the current work.

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