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.
@Article{Keane2015ac, author = {Jonathan Keane and Zed Sevcikova and Karen Emmorey and Diane Brentari}, doi = {10.1017/S0952675717000124}, journal = {Phonology}, month = {August}, number = {2}, pages = {221––241}, title = {A Theory-Driven Model of Handshape Similarity}, volume = {34}, year = {2017}, bdsk-file-1 = {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}, }