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Supervised and unsupervised learning of multidimensionally varying nonnative speech categories

MPG-Autoren

Goudbeek,  Martijn
Language Comprehension Group, MPI for Psycholinguistics, Max Planck Society;
Phonological Learning for Speech Perception, MPI for Psycholinguistics, Max Planck Society;

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Cutler,  Anne
Language Comprehension Group, MPI for Psycholinguistics, Max Planck Society;
Phonological Learning for Speech Perception, MPI for Psycholinguistics, Max Planck Society;

Smits,  Roel
Language Comprehension Group, MPI for Psycholinguistics, Max Planck Society;
Phonological Learning for Speech Perception, MPI for Psycholinguistics, Max Planck Society;

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Goudbeek_2008_supervised.pdf
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Zitation

Goudbeek, M., Cutler, A., & Smits, R. (2008). Supervised and unsupervised learning of multidimensionally varying nonnative speech categories. Speech Communication, 50(2), 109-125. doi:10.1016/j.specom.2007.07.003.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-1FD4-5
Zusammenfassung
The acquisition of novel phonetic categories is hypothesized to be affected by the distributional properties of the input, the relation of the new categories to the native phonology, and the availability of supervision (feedback). These factors were examined in four experiments in which listeners were presented with novel categories based on vowels of Dutch. Distribution was varied such that the categorization depended on the single dimension duration, the single dimension frequency, or both dimensions at once. Listeners were clearly sensitive to the distributional information, but unidimensional contrasts proved easier to learn than multidimensional. The native phonology was varied by comparing Spanish versus American English listeners. Spanish listeners found categorization by frequency easier than categorization by duration, but this was not true of American listeners, whose native vowel system makes more use of duration-based distinctions. Finally, feedback was either available or not; this comparison showed supervised learning to be significantly superior to unsupervised learning.