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dc.contributor.advisorFeldman, Naomien_US
dc.contributor.authorKronrod, Yakoven_US
dc.date.accessioned2015-02-06T06:37:02Z
dc.date.available2015-02-06T06:37:02Z
dc.date.issued2014en_US
dc.identifierhttps://doi.org/10.13016/M2K32Q
dc.identifier.urihttp://hdl.handle.net/1903/16164
dc.description.abstractIn this dissertation I present a model that captures categorical effects in both first language (L1) and second language (L2) speech perception. In L1 perception, categorical effects range between extremely strong for consonants to nearly continuous perception of vowels. I treat the problem of speech perception as a statistical inference problem and by quantifying categoricity I obtain a unified model of both strong and weak categorical effects. In this optimal inference mechanism, the listener uses their knowledge of categories and the acoustics of the signal to infer the intended productions of the speaker. The model splits up speech variability into meaningful category variance and perceptual noise variance. The ratio of these two variances, which I call Tau, directly correlates with the degree of categorical effects for a given phoneme or continuum. By fitting the model to behavioral data from different phonemes, I show how a single parametric quantitative variation can lead to the different degrees of categorical effects seen in perception experiments with different phonemes. In L2 perception, L1 categories have been shown to exert an effect on how L2 sounds are identified and how well the listener is able to discriminate them. Various models have been developed to relate the state of L1 categories with both the initial and eventual ability to process the L2. These models largely lacked a formalized metric to measure perceptual distance, a means of making a-priori predictions of behavior for a new contrast, and a way of describing non-discrete gradient effects. In the second part of my dissertation, I apply the same computational model that I used to unify L1 categorical effects to examining L2 perception. I show that we can use the model to make the same type of predictions as other SLA models, but also provide a quantitative framework while formalizing all measures of similarity and bias. Further, I show how using this model to consider L2 learners at different stages of development we can track specific parameters of categories as they change over time, giving us a look into the actual process of L2 category development.en_US
dc.language.isoenen_US
dc.titleBayesian Model of Categorical Effects in L1 and L2 Speech Processingen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentLinguisticsen_US
dc.subject.pqcontrolledLinguisticsen_US
dc.subject.pqcontrolledCognitive psychologyen_US
dc.subject.pqcontrolledAcousticsen_US
dc.subject.pquncontrolledBayesianen_US
dc.subject.pquncontrolledcategorical perceptionen_US
dc.subject.pquncontrolledcomputational phonologyen_US
dc.subject.pquncontrolledperceptual magnet effecten_US
dc.subject.pquncontrolledsecond language acquisitionen_US
dc.subject.pquncontrolledspeech perceptionen_US


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