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

Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than non-native ones (1–3). For example, between 6-8 months and 10-12 months, infants learning American English get better at distinguishing English [ɹ] and [l], as in ‘rock’ vs ‘lock’, relative to infants learning Japanese (4). Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories—like [ɹ] and [l] in English—through a statistical clustering mechanism dubbed ‘distributional learning’ (5–8). The feasibility of this mechanism for learning phonetic categories has been challenged, however (9–16). 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 novel mechanismdriven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows, for the first time, accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants’ attunement.