Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders

By Cory Shain, Micha Elsner. 2019

23 citations recorded by the source corpus.

In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theory-driven phonological features are encoded in the latent bit patterns, finding that some (e.g. [+-approximant]), are well represented by the network in both languages, while others (e.g. [+-spread glottis]) are less so. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition.

Subfields: Representation & Self-Supervised Learning, Cognitive Science & Psychology

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Citations in the corpus

Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks (2019, background): “From a scientiﬁc perspective, discrete representations could be useful in cognitive models to study phonetic category learning in human infants [15–18].”