Evaluating computational models of infant phonetic learning across languages

Yevgen Matusevych, Thomas Schatz, Herman Kamper, Naomi H. Feldman, Sharon Goldwater

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


In the first year of life, infants’ speech perception becomes attuned to the sounds of their native language. Many accounts of this early phonetic learning exist, but computational models predicting the attunement patterns observed in infants from the speech input they hear have been lacking. A recent study presented the first such model, drawing on algorithms proposed for unsupervised learning from naturalistic speech, and tested it on a single phone contrast. Here we study five such algorithms, selected for their potential cognitive relevance. We simulate phonetic learning with each algorithm and perform tests on three phone contrasts from different languages, comparing the results to infants’ discrimination patterns. The five models display varying degrees of agreement with empirical observations, showing that our approach can help decide between candidate mechanisms for early phonetic learning, and providing insight into which aspects of the models are critical for capturing infants’ perceptual development.
Original languageEnglish
Title of host publicationProceedings of the 42nd Annual Conference of the Cognitive Science Society
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
  • representation learning
  • phone discrimination
  • computational model


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