气候变化领域集成服务门户(CCPortal): Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input

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DOI	10.1073/pnas.2001844118
	Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input
	Schatz T.; Feldman N.H.; Goldwater S.; Cao X.-N.; Dupoux E.
发表日期	2021
ISSN	00278424
卷号	118 期号:7
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Phonetic learning \| language acquisition \| computational modeling
语种	英语
scopus关键词	article; computer model; Englishman; female; human; human experiment; infant; language; learning algorithm; male; prediction; quantitative analysis; simulation; speech
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180674
作者单位	Department of Linguistics, University of Maryland, College Park, MD 20742, United States; University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, United States; School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom; Cognitive Machine Learning, École Normale Supérieure–École des Hautes Études en Sciences Sociales–Paris Sciences et Lettres Research University, CNRS, Institut National de Recherche en Informatique et en Automatique, 75012 Paris, France;, eFacebook A.I. Research, Paris, 75002, France
推荐引用方式 GB/T 7714	Schatz T.,Feldman N.H.,Goldwater S.,et al. Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input[J],2021,118(7).
APA	Schatz T.,Feldman N.H.,Goldwater S.,Cao X.-N.,&Dupoux E..(2021).Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.Proceedings of the National Academy of Sciences of the United States of America,118(7).
MLA	Schatz T.,et al."Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input".Proceedings of the National Academy of Sciences of the United States of America 118.7(2021).