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Artificial Language Learning Of Vowel Reduction by Benjamin Storme
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1Artificial Language Learning Of Vowel Reduction
By Benjamin Storme and Francesc Torres-Tamarit
Amendment to the original preregistration.
“Artificial Language Learning Of Vowel Reduction” Metadata:
- Title: ➤ Artificial Language Learning Of Vowel Reduction
- Authors: Benjamin StormeFrancesc Torres-Tamarit
Edition Identifiers:
- Internet Archive ID: osf-registrations-3qxnb-v1
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The book is available for download in "data" format, the size of the file-s is: 0.17 Mbs, the file-s for this book were downloaded 3 times, the file-s went public at Fri Aug 20 2021.
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2Artificial Language Learning Of Vowel Reduction
By Benjamin Storme and Francesc Torres-Tamarit
One typologically common pattern of vowel reduction involves raising of unstressed vowels. The opposite pattern whereby vowels are lowered in unstressed syllables is cross-linguistically rare. There is a phonetic motivation for this asymmetry: unstressed vowels being shorter undershoot their F1 targets as compared to stressed vowels, and therefore get more assimilated to surrounding consonants. In so far as consonants generally have lower F1 targets than vowels, this assimilation results in lower F1 realizations for unstressed vowels (i.e. in vowel raising). This study aims to test whether this typological asymmetry is reflected in language learning, with an artificial language raising mid vowels in unstressed syllables (e.g. singular [péka], plural [pikáta]) being easier to learn than a language lowering high vowels in unstressed syllables (e.g. singular [píka], plural [pekáta]). We will test two populations, one having French as L1 and the other one having Spanish as L1. None of these two languages display phonological vowel reduction of the sort described before. We choose two populations to discard potential language-particular effects.
“Artificial Language Learning Of Vowel Reduction” Metadata:
- Title: ➤ Artificial Language Learning Of Vowel Reduction
- Authors: Benjamin StormeFrancesc Torres-Tamarit
Edition Identifiers:
- Internet Archive ID: osf-registrations-e3pn5-v1
Downloads Information:
The book is available for download in "data" format, the size of the file-s is: 0.09 Mbs, the file-s for this book were downloaded 2 times, the file-s went public at Fri Sep 10 2021.
Available formats:
Archive BitTorrent - Metadata - ZIP -
Related Links:
- Whefi.com: Download
- Whefi.com: Review - Coverage
- Internet Archive: Details
- Internet Archive Link: Downloads
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