Statistical learning of phonetic categories: insights from a computational approach Export

@article{citeulike:4272693, abstract = {Recent evidence (Maye, Werker \& Gerken, 2002) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model based on a mixture of Gaussians (MOG) architecture. Statistical learning alone was found to be insufficient for phonetic category learning 2013 an additional competition mechanism was required in order for the categories in the input to be successfully learnt. When competition was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity to phonetic contrasts. Moreover, the MOG with competition model was used to explore a potentially important distributional property of early speech categories 2013 sparseness 2013 in which portions of the space between phonetic categories are unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favoured continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed.}, address = {.Department of Psychology and the Delta Center, University of Iowa, Iowa City, USA; .Department of Brain and Cognitive Sciences, University of Rochester, Rochester, USA}, author = {Mcmurray, Bob and Aslin, Richard N. and Toscano, Joseph C.}, citeulike-article-id = {4272693}, citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1467-7687.2009.00822.x}, citeulike-linkout-1 = {http://www.ingentaconnect.com/content/bpl/desc/2009/00000012/00000003/art00003}, citeulike-linkout-2 = {http://www3.interscience.wiley.com/cgi-bin/abstract/122267496/ABSTRACT}, doi = {10.1111/j.1467-7687.2009.00822.x}, issn = {1363-755X}, journal = {Developmental Science}, keywords = {categories, language, learning, model, modeling, models, phonetic\_categories, speech, speech\_perception, statistical\_learning}, number = {3}, pages = {369--378}, posted-at = {2009-06-30 20:42:26}, priority = {2}, publisher = {Blackwell Publishing}, title = {Statistical learning of phonetic categories: insights from a computational approach}, url = {http://dx.doi.org/10.1111/j.1467-7687.2009.00822.x}, volume = {12}, year = {2009} }

TY - JOUR ID - citeulike:4272693 L3 - citeulike-article-id:4272693 N2 - Recent evidence (Maye, Werker & Gerken, 2002) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model based on a mixture of Gaussians (MOG) architecture. Statistical learning alone was found to be insufficient for phonetic category learning 2013 an additional competition mechanism was required in order for the categories in the input to be successfully learnt. When competition was added to the MOG architecture, this class of models successfully accounted for developmental enhancement and loss of sensitivity to phonetic contrasts. Moreover, the MOG with competition model was used to explore a potentially important distributional property of early speech categories 2013 sparseness 2013 in which portions of the space between phonetic categories are unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favoured continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed. IS - 3 JF - Developmental Science SN - 1363-755X AD - .Department of Psychology and the Delta Center, University of Iowa, Iowa City, USA; .Department of Brain and Cognitive Sciences, University of Rochester, Rochester, USA EP - 378 TI - Statistical learning of phonetic categories: insights from a computational approach PB - Blackwell Publishing VL - 12 SP - 369 KW - categories KW - language KW - learning KW - model KW - modeling KW - models KW - phonetic_categories KW - speech KW - speech_perception KW - statistical_learning AU - Mcmurray, Bob AU - Aslin, Richard AU - Toscano, Joseph PY - 2009/// UR - http://dx.doi.org/10.1111/j.1467-7687.2009.00822.x ER -