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

Abstract

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 replicate 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 cross-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 is 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.

Computational Modeling of the Segmentation of Sentence Stimuli From an Infant Word‐Finding Study

“…Our work here is, fundamentally, a demonstration project, joining other work that aims to harness developments in computational speech technology (e.g., Elsner, Goldwater, & Eisenstein, 2012;Matusevych, Schatz, Kamper, Feldman, & Goldwater, 2023;Räsänen & Rasilo, 2015;Roy & Pentland, 2002) with precursors in the machine learning literature (e.g., Harwath, Torralba, & Glass, 2016). We view our approach as contrasting with the direction that language acquisition research usually takes.…”