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Title: A phonetic model of non-native spoken word processing
Non-native speakers show difficulties with spoken word processing. Many studies attribute these difficulties to imprecise phonological encoding of words in the lexical memory. We test an alternative hypothesis: that some of these difficulties can arise from the non-native speakers' phonetic perception. We train a computational model of phonetic learning, which has no access to phonology, on either one or two languages. We first show that the model exhibits predictable behaviors on phone-level and word-level discrimination tasks. We then test the model on a spoken word processing task, showing that phonology may not be necessary to explain some of the word processing effects observed in non-native speakers. We run an additional analysis of the model's lexical representation space, showing that the two training languages are not fully separated in that space, similarly to the languages of a bilingual human speaker.  more » « less
Award ID(s):
1734245
NSF-PAR ID:
10226921
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics
Page Range / eLocation ID:
1480-1490
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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