Unsupervised learning of vowel categories from infant-directed speech.

ABSTRACT: Infants rapidly learn the sound categories of their native language, even though they do not receive explicit or focused training. Recent research suggests that this learning is due to infants' sensitivity to the distribution of speech sounds and that infant-directed speech contains the distributional information needed to form native-language vowel categories. An algorithm, based on Expectation-Maximization, is presented here for learning the categories from a sequence of vowel tokens without (i) receiving any category information with each vowel token, (ii) knowing in advance the number of categories to learn, or (iii) having access to the entire data ensemble. When exposed to vowel tokens drawn from either English or Japanese infant-directed speech, the algorithm successfully discovered

SUBMITTER: Vallabha GK 

PROVIDER: S-EPMC1934922 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature