Modeling the acquisition of covert contrast

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper explores the learnability of covert contrasts (impressionistically homophonous categories that can be reliably distinguished at the phonetic level) through a series of model-based clustering simulations using human production data. Allowing the models to learn both the number and parameters of those categories provides a way to explore the potential stability of category structures. The results indicate that while a statistical learner can be quite effective at inducing covert contrasts, success depends crucially on the number and distributional characteristics of the relevant cue dimensions
Original languageEnglish
Title of host publicationProceedings of the 17th International Congress of Phonetic Sciences
EditorsWai-Sum Lee, Eric Zee
Place of PublicationHong Kong
Number of pages4
Publication statusPublished - 2011


  • Phonetics
  • Dutch
  • Phonology
  • statistical modeling


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