Unsupervised learning of vowel categories from infant-directed speech Export

@article{citeulike:5831450, abstract = {10.1073/pnas.0705369104 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\^{a}€“Maximization, is presented here for learning the categories from a sequence of vowel tokens without () receiving any category information with each vowel token, () knowing in advance the number of categories to learn, or () 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 the language-specific vowel categories (/, i, \^{I}µ, e/ for English, /i, i\"{E}, e, e\"{E}/ for Japanese). A nonparametric version of the algorithm, closely related to neural network models based on topographic representation and competitive Hebbian learning, also was able to discover the vowel categories, albeit somewhat less reliably. These results reinforce the proposal that native-language speech categories are acquired through distributional learning and that such learning may be instantiated in a biologically plausible manner.}, author = {Vallabha, Gautam K. and McClelland, James L. and Pons, Ferran and Werker, Janet F. and Amano, Shigeaki}, citeulike-article-id = {5831450}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0705369104}, citeulike-linkout-1 = {http://www.pnas.org/content/104/33/13273.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/104/33/13273.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17664424}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17664424}, day = {14}, doi = {10.1073/pnas.0705369104}, journal = {Proceedings of the National Academy of Sciences}, keywords = {categories, computational-model, distributional-cues, expectation-maximization, infant-directed-speech, model, phoneme-recognition, unsupervised-learning, vowels}, month = {August}, number = {33}, pages = {13273--13278}, posted-at = {2009-09-23 16:27:02}, priority = {4}, title = {Unsupervised learning of vowel categories from infant-directed speech}, url = {http://dx.doi.org/10.1073/pnas.0705369104}, volume = {104}, year = {2007} }

TY - JOUR ID - citeulike:5831450 L3 - citeulike-article-id:5831450 N2 - 10.1073/pnas.0705369104 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 () receiving any category information with each vowel token, () knowing in advance the number of categories to learn, or () 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 the language-specific vowel categories (/, i, ε, e/ for English, /i, iː, e, eː/ for Japanese). A nonparametric version of the algorithm, closely related to neural network models based on topographic representation and competitive Hebbian learning, also was able to discover the vowel categories, albeit somewhat less reliably. These results reinforce the proposal that native-language speech categories are acquired through distributional learning and that such learning may be instantiated in a biologically plausible manner. IS - 33 JF - Proceedings of the National Academy of Sciences EP - 13278 TI - Unsupervised learning of vowel categories from infant-directed speech VL - 104 SP - 13273 KW - categories KW - computational-model KW - distributional-cues KW - expectation-maximization KW - infant-directed-speech KW - model KW - phoneme-recognition KW - unsupervised-learning KW - vowels AU - Vallabha, Gautam AU - McClelland, James AU - Pons, Ferran AU - Werker, Janet AU - Amano, Shigeaki PY - 2007/08/14/ UR - http://dx.doi.org/10.1073/pnas.0705369104 ER -