A phonetic model of non-native spoken word processing
This work addresses the challenge of understanding language processing in non-native speakers, but it is incremental as it builds on existing theories without introducing a major breakthrough.
The study tackled the problem of non-native spoken word processing difficulties by testing a phonetic perception hypothesis with a computational model, showing that phonology may not be necessary to explain some observed effects and that the model's lexical representations resemble bilingual human patterns.
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.