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Input matters in the modeling of early phonetic learning

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Abstract

In acquiring language, differences in input can greatly affectlearning outcomes, but which aspects of language learning aremost sensitive to input variations, and which are robust, remainsdebated. A recent modeling study successfully reproduced aphenomenon empirically observed in early phonetic learning—learning about the sounds of the native language in the firstyear of life—despite using input that differed in quantity andspeaker composition from what a typical infant would hear. Inthis paper, we carry out a direct test of that model’s robustnessto input variations. We find that, despite what the original resultsuggested, the learning outcomes are sensitive to properties ofthe input and that more plausible input leads to a better fit withempirical observations. This has implications for understandingearly phonetic learning in infants and underscores the impor-tance of using realistic input in models of language acquisition.

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