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A theory-driven model of handshape similarity*

Published online by Cambridge University Press:  14 August 2017

Jonathan Keane ,
Zed Sevcikova Sehyr ,
Karen Emmorey  and
Diane Brentari
Jonathan Keane*
Affiliation:
University of Chicago
Zed Sevcikova Sehyr*
Affiliation:
San Diego State University
Karen Emmorey*
Affiliation:
San Diego State University
Diane Brentari*
Affiliation:
University of Chicago
*
E-mail: jonkeane@uchicago.edu, zsevcikova@mail.sdsu.edu, kemmorey@mail.sdsu.edu, dbrentari@uchicago.edu.
E-mail: jonkeane@uchicago.edu, zsevcikova@mail.sdsu.edu, kemmorey@mail.sdsu.edu, dbrentari@uchicago.edu.
E-mail: jonkeane@uchicago.edu, zsevcikova@mail.sdsu.edu, kemmorey@mail.sdsu.edu, dbrentari@uchicago.edu.
E-mail: jonkeane@uchicago.edu, zsevcikova@mail.sdsu.edu, kemmorey@mail.sdsu.edu, dbrentari@uchicago.edu.
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Abstract

Following the Articulatory Model of Handshape (Keane 2014), which mathematically defines handshapes on the basis of joint angles, we propose two methods for calculating phonetic similarity: a contour difference method, which assesses the amount of change between handshapes within a fingerspelled word, and a positional similarity method, which compares similarity between pairs of letters in the same position across two fingerspelled words. Both methods are validated with psycholinguistic evidence based on similarity ratings by deaf signers. The results indicate that the positional similarity method more reliably predicts native signer intuition judgements about handshape similarity. This new similarity metric fills a gap in the literature (the lack of a theory-driven similarity metric) that has been empty since effectively the beginning of sign-language linguistics.

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Type
Articles
Information
Phonology , Volume 34 , Issue 2 , August 2017 , pp. 221 - 241
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

This work would not have been possible without the contributions of the Deaf signers who participated in our experiments. The manuscript greatly benefited from the feedback of our colleagues Jordan Fenlon, Leah Geer and Jason Riggle, as well as the anonymous reviewers. All mistakes and omissions are our own. This work was also supported in part by a Doctoral Dissertation Research Improvement Grant (NSF BCS 1251807) and a NSF Research Grant (IIS 1409886).

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