Input matters in the modeling of early phonetic learning

Ruolan Li, Thomas Schatz, Yevgen Matusevych, Sharon Goldwater, Naomi H. Feldman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In acquiring language, differences in input can greatly affect learning outcomes, but which aspects of language learning are most sensitive to input variations, and which are robust, remains debated. A recent modeling study successfully reproduced a phenomenon empirically observed in early phonetic learning—learning about the sounds of the native language in the first year of life—despite using input that differed in quantity and speaker composition from what a typical infant would hear. In this paper, we carry out a direct test of that model’s robustness to input variations. We find that, despite what the original result suggested, the learning outcomes are sensitive to properties of the input and that more plausible input leads to a better fit with empirical observations. This has implications for understanding early phonetic learning in infants and underscores the importance of using realistic input in models of language acquisition.
Original languageEnglish
Title of host publicationProceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society 2020
EditorsStephanie Denison, Michael Mack, Yang Xu, Blair C. Armstrong
PublisherCognitive Science Society
Number of pages7
Publication statusPublished - 1 Aug 2020
Event42nd Annual Virtual Meeting of the Cognitive Science Society - Virtual Meeting, Toronto, Canada
Duration: 29 Jul 20201 Aug 2020


Conference42nd Annual Virtual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2020
Internet address


  • early phonetic learning
  • computational modeling
  • input variation
  • speech perception


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