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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 Issues in Uyghur backness harmony: Corpus, experimental, and computational studies Mayer, Connor. - : eScholarship, University of California, 2021 BASE Show details 2 Capturing gradience in long-distance phonology using probabilistic tier-based strictly local grammars ... Mayer, Connor. - : University of Massachusetts Amherst, 2021 BASE Show details 3 Capturing gradience in long-distance phonology using probabilistic tier-based strictly local grammars Mayer, Connor In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 4 Quantal biomechanical effects in speech postures of the lips. Gick, Bryan; Mayer, Connor; Chiu, Chenhao... In: Journal of neurophysiology, vol 124, iss 3 (2020) BASE Show details 5 A Method for Projecting Features from Observed Sets of Phonological Classes Mayer, Connor; Daland, Robert In: LINGUISTIC INQUIRY, vol 51, iss 4 (2020) BASE Show details 6 Quantal biomechanical effects in speech postures of the lips Gick, Bryan; Mayer, Connor; Chiu, Chenhao... In: J Neurophysiol (2020) BASE Show details 7 Phonotactic learning with neural language models Mayer, Connor; Nelson, Max In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 8 An Algorithm for Learning Phonological Classes from Distributional Similarity Mayer, Connor. - : eScholarship, University of California, 2018 In: Mayer, Connor. (2018). An Algorithm for Learning Phonological Classes from Distributional Similarity. UCLA: Linguistics 0510. Retrieved from: http://www.escholarship.org/uc/item/5jp6q2xn (2018) Abstract: An outstanding question in phonology is to what degree the learner uses distributional information rather than substantive properties of speech sounds when learning phonological structure. This paper presents an algorithm that learns phonological classes from only distributional information: the contexts in which sounds occur. The input is a segmental corpus, and the output is a set of phonological classes. The algorithm is first tested on an artificial language with both overlapping and nested classes reflected in the distribution. It retrieves the expected classes, and performs well as distributional noise is added. It is then tested on four natural languages. It distinguishes between consonants and vowels in all cases, and finds more detailed, language-specific structure. These results improve on past approaches, and are encouraging given the paucity of the input. Further refined models may provide additional insight into which phonological classes are apparent in the distributions of sounds in natural languages. Keyword: computational phonology; distributional learning; emergent phonology; Linguistics; phonological classes; phonology URL: http://www.escholarship.org/uc/item/5jp6q2xn BASE Hide details 9 An Algorithm for Learning Phonological Classes from Distributional Similarity ... Mayer, Connor. - : Unpublished, 2018 BASE Show details 10 Perceptual Integration of Visual Evidence of the Airstream from Aspirated Stops Mayer, Connor; Gick, Bryan; Weigel, Tamra. - 2013 BASE Show details 11 Talking while chewing: speaker response to natural perturbation of speech Gick, Bryan; Mayer, Connor In: Phonetica. - Berlin : De Gruyter Mouton 69 (2012) 3, 109-123 BLLDB OLC Linguistik Show details

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