A phonetic model of non-native spoken word processing

Yevgen Matusevych, Herman Kamper, Thomas Schatz, Naomi Feldman, Sharon Goldwater

Abstract
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.
Anthology ID:
2021.eacl-main.127
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1480–1490
Language:
URL:
https://aclanthology.org/2021.eacl-main.127
DOI:
10.18653/v1/2021.eacl-main.127
Award:
 Honorable Mention for Best Long Paper
Bibkey:
Cite (ACL):
Yevgen Matusevych, Herman Kamper, Thomas Schatz, Naomi Feldman, and Sharon Goldwater. 2021. A phonetic model of non-native spoken word processing. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1480–1490, Online. Association for Computational Linguistics.
Cite (Informal):
A phonetic model of non-native spoken word processing (Matusevych et al., EACL 2021)
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PDF:
https://preview.aclanthology.org/add_acl24_videos/2021.eacl-main.127.pdf
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