Does Infant-Directed Speech Help Phonetic Learning? A Machine Learning Investigation.

Journal Article (Journal Article)

A prominent hypothesis holds that by speaking to infants in infant-directed speech (IDS) as opposed to adult-directed speech (ADS), parents help them learn phonetic categories. Specifically, two characteristics of IDS have been claimed to facilitate learning: hyperarticulation, which makes the categories more separable, and variability, which makes the generalization more robust. Here, we test the separability and robustness of vowel category learning on acoustic representations of speech uttered by Japanese adults in ADS, IDS (addressed to 18- to 24-month olds), or read speech (RS). Separability is determined by means of a distance measure computed between the five short vowel categories of Japanese, while robustness is assessed by testing the ability of six different machine learning algorithms trained to classify vowels to generalize on stimuli spoken by a novel speaker in ADS. Using two different speech representations, we find that hyperarticulated speech, in the case of RS, can yield better separability, and that increased between-speaker variability in ADS can yield, for some algorithms, more robust categories. However, these conclusions do not apply to IDS, which turned out to yield neither more separable nor more robust categories compared to ADS inputs. We discuss the usefulness of machine learning algorithms run on real data to test hypotheses about the functional role of IDS.

Full Text

Duke Authors

Cited Authors

  • Ludusan, B; Mazuka, R; Dupoux, E

Published Date

  • May 1, 2021

Published In

Volume / Issue

  • 45 / 5

Start / End Page

  • e12946 -

PubMed ID

  • 34018231

Electronic International Standard Serial Number (EISSN)

  • 1551-6709

International Standard Serial Number (ISSN)

  • 0364-0213

Digital Object Identifier (DOI)

  • 10.1111/cogs.12946

Language

  • eng