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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) Abstract: Computational models of phonotactics share much in common with language models, which assign probabilities to sequences of words. While state of the art language models are implemented using neural networks, phonotactic models have not followed suit. We present several neural models of phonotactics, and show that they perform favorably when compared to existing models. In addition, they provide useful insights into the role of representations on phonotactic learning and generalization. This work provides a promising starting point for future modeling of human phonotactic knowledge. Keyword: Computational Linguistics; neural networks; phonology; phonotactics; sonority sequencing URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1132&context=scil https://scholarworks.umass.edu/scil/vol3/iss1/16 BASE Hide 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) BASE Show 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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