A phonetic model of non-native spoken word processing

TL;DR

This paper proposes a phonetic perception-based computational model to explain non-native spoken word processing difficulties, challenging the traditional phonological encoding explanation and showing that phonology may not be essential.

Contribution

It introduces a phonetic learning model without phonological access, demonstrating its ability to replicate non-native word processing behaviors and revealing bilingual-like lexical representations.

Findings

Model exhibits predictable phone and word discrimination behaviors.

Phonology may not be necessary to explain some non-native word processing effects.

Lexical space shows partial separation similar to bilingual speakers.

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.