当前位置： SCI文献检索 > PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA期刊下所有文献 > Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

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

Abstract：

: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.

journal_name

Proc Natl Acad Sci U S A

journal_title

Proceedings of the National Academy of Sciences of the United States of America

authors

Schatz T,Feldman NH,Goldwater S,Cao XN,Dupoux E

doi

10.1073/pnas.2001844118

subject

Has Abstract

pub_date

2021-02-09 00:00:00

issue

7

eissn

0027-8424

issn

1091-6490

pii

2001844118

journal_volume

118

pub_type

杂志文章

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