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

Cory Shain, Micha Elsner


Abstract
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.
Anthology ID:
N19-1007
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
69–85
Language:
URL:
https://aclanthology.org/N19-1007
DOI:
10.18653/v1/N19-1007
Bibkey:
Cite (ACL):
Cory Shain and Micha Elsner. 2019. Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 69–85, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (Shain & Elsner, NAACL 2019)
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PDF:
https://aclanthology.org/N19-1007.pdf
Video:
 https://vimeo.com/353440477