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Normalization may be ineffective for phonetic category learning ...
Hitczenko, Kasia; Mazuka, Reiko; Elsner, Micha. - : University of Massachusetts Amherst, 2019
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Normalization may be ineffective for phonetic category learning
In: Proceedings of the Society for Computation in Linguistics (2019)
Abstract: Sound categories often overlap in their acoustics, which can make phonetic learning difficult. Several studies argued that normalizing acoustics relative to context improves category separation (e.g. Dillon et al., 2013). However, recent work shows that normalization is ineffective for learning Japanese vowel length from spontaneous child-directed speech (Hitczenko et al., 2018). We show that this discrepancy arises from differences between spontaneous and controlled lab speech, and that normalization can increase category overlap when there are regularities in which contexts different sounds occur in - a hallmark of spontaneous speech. Therefore, normalization is unlikely to help in real, naturalistic phonetic learning situations.
Keyword: Computational Linguistics; normalization; phonetic learning
URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1058&context=scil
https://scholarworks.umass.edu/scil/vol2/iss1/51
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Proceedings of the 41th Annual Boston University Conference on Language Development [held November 4-6, 2016, in Boston] 1. 1
In: 1 (2017), S. 32-45
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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