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        Highly complex syllable structure

        A typological and diachronic study

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        Author(s)
        Easterday, Shelece
        Collection
        Knowledge Unlatched (KU); Language Science Press 2018-2020
        Number
        104957
        Language
        English
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        Abstract
        The syllable is a natural unit of organization in spoken language whose strongest cross-linguistic patterns are often explained in terms of a universal preference for the CV structure. Syllable patterns involving long sequences of consonants are both typologically rare and theoretically marginalized, with few approaches treating these as natural or unproblematic structures. This book is an investigation of the properties of languages with highly complex syllable patterns. The two aims are (i) to establish whether these languages share other linguistic features in common such that they constitute a distinct linguistic type, and (ii) to identify possible diachronic paths and natural mechanisms by which these patterns come about in the history of a language. These issues are investigated in a diversified sample of 100 languages, 25 of which have highly complex syllable patterns.
        URI
        http://library.oapen.org/handle/20.500.12657/23420
        Keywords
        Linguistics
        DOI
        10.5281/zenodo.3268721
        ISBN
        9783961101948
        Publisher
        Language Science Press
        Publisher website
        https://langsci-press.org/
        Publication date and place
        Berlin, 2019-11-13
        Grantor
        • Knowledge Unlatched - 104957 - Language Science Press 2018 - 2020
        Series
        Studies in Laboratory Phonology,
        Classification
        Linguistics
        Rights
        https://creativecommons.org/licenses/by/4.0/legalcode
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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