Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input

By Thomas Schatz, Naomi H Feldman, Sharon Goldwater, Xuan-Nga Cao, Emmanuel Dupoux. 2021

81 citations recorded by the source corpus.

Significance Infants become attuned to the sounds of their native language(s) before they even speak. Hypotheses about what is being learned by infants have traditionally driven researchers’ attempts to understand this surprising phenomenon. Here, we propose to start, instead, from hypotheses about how infants might learn. To implement this mechanism-driven approach, we introduce a quantitative modeling framework based on large-scale simulation of the learning process on realistic input. It allows learning mechanisms to be systematically linked to testable predictions regarding infants’ attunement to their native language(s). Through this framework, we obtain evidence for an account of infants’ attunement that challenges established theories about what infants are learning. Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get better at distinguishing English and [l], as in “rock” vs. “lock,” relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories—like and [l] in English—through a statistical clustering mechanism dubbed “distributional learning.” The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here, we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning, as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that, contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants’ attunement.

Subfields: Cultural Studies, Developmental and Educational Psychology, Pharmacy

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