Infant phonetic learning as perceptual space learning: A crosslinguistic evaluation of computational models

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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In the first year of life, infants’ speech perception becomes attuned to the sounds of their native language. This process of early phonetic learning has traditionally been framed as phonetic category acquisition. However, recent studies have hypothesized that the attunement may instead reflect a perceptual space learning process that does not involve categories. In this article, we explore the idea of perceptual space learning by implementing five different perceptual space learning models and testing them on three phonetic contrasts that have been tested in the infant speech perception literature. We reproduce and extend previous results showing that a perceptual space learning model that uses only distributional information about the acoustics of short time slices of speech can account for at least some cross-linguistic differences in infant perception. Moreover, we find that a second perceptual space learning model which benefits from word-level guidance performs equally well in capturing cross13 linguistic differences in infant speech perception. These results provide support for the general idea of perceptual space learning as a theory of early phonetic learning, but suggest that more fine-grained data are needed to distinguish between different formal accounts. Finally, we provide testable empirical predictions of the two most promising models and show that these are not identical, making it possible to independently evaluate each model in experiments with infants in future research.
Original languageEnglish
Article numbere13314
Pages (from-to)1-39
JournalCognitive Science: A Multidisciplinary Journal
Issue number7
Early online date18 Jul 2023
Publication statusPublished - Jul 2023

Keywords / Materials (for Non-textual outputs)

  • phonetic learning
  • computational modelling
  • perceptual space
  • language acquisition
  • phone discrimination
  • speech perception


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  • Input matters in the modeling of early phonetic learning

    Li, R., Schatz, T., Matusevych, Y., Goldwater, S. & Feldman, N. H., 1 Aug 2020, Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society 2020. Denison, S., Mack, M., Xu, Y. & Armstrong, B. C. (eds.). Cognitive Science Society, 7 p.

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

    Open Access

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