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Electrophysiological Evidence for Top-Down Lexical Influences on Early Speech Perception ...
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GetzSupplementalMaterial_rev – Supplemental material for Electrophysiological Evidence for Top-Down Lexical Influences on Early Speech Perception ...
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GetzSupplementalMaterial_rev – Supplemental material for Electrophysiological Evidence for Top-Down Lexical Influences on Early Speech Perception ...
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Electrophysiological Evidence for Top-Down Lexical Influences on Early Speech Perception ...
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The time-course of cortical responses to speech revealed by fast optical imaging
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The time-course of speaking rate compensation: Effects of sentential rate and vowel length on voicing judgments
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Cue integration and context effects in speech: Evidence against speaking rate normalization
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Cue integration with categories: Weighting acoustic cues in speech using unsupervised learning and distributional statistics
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Continuous perception and graded categorization: Electrophysiological evidence for a linear relationship between the acoustic signal and perceptual encoding of speech
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Statistical learning of phonetic categories: Insights from a computational approach
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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—an additional competition mechanism was required in order to successfully learn the categories in the input. 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 -- sparseness -- in which portions of the space between phonetic categories is unmapped. Sparseness was found in all successful models and quickly emerged during development even when the initial parameters favored continuous representations with no gaps. The implications of these models for phonetic category learning in infants are discussed.
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Article
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URL: http://www.ncbi.nlm.nih.gov/pubmed/19371359 https://doi.org/10.1111/j.1467-7687.2009.00822.x http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2742678
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Integrating Connectionist Learning and Dynamic Processing: Case Studies in Speech and Lexical Development
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