  In the first year of life, infants' speech perception becomes attuned to the
sounds of their native language. Many accounts of this early phonetic learning
exist, but computational models predicting the attunement patterns observed in
infants from the speech input they hear have been lacking. A recent study
presented the first such model, drawing on algorithms proposed for unsupervised
learning from naturalistic speech, and tested it on a single phone contrast.
Here we study five such algorithms, selected for their potential cognitive
relevance. We simulate phonetic learning with each algorithm and perform tests
on three phone contrasts from different languages, comparing the results to
infants' discrimination patterns. The five models display varying degrees of
agreement with empirical observations, showing that our approach can help
decide between candidate mechanisms for early phonetic learning, and providing
insight into which aspects of the models are critical for capturing infants'
perceptual development.
