Kempton, T. and Moore, R.K. (2013) Discovering the phoneme inventory of an unwritten language: A machine-assisted approach. Speech Communication, 56. 152 - 166. ISSN 0167-6393
Abstract
There is a consensus between many linguists that half of all languages risk disappearing by the end of the century. Documentation is agreed to be a priority. This includes the process of phonemic analysis to discover the contrastive sounds of a language with the resulting benefits of further linguistic analysis, literacy, and access to speech technology. A machine-assisted approach to phonemic analysis has the potential to greatly speed up the process and make the analysis more objective.
It is demonstrated that a machine-assisted approach can make a measurable contribution to a phonemic analysis for all the procedures investigated; phonetic similarity, complementary distribution, and minimal pairs. The evaluation measures introduced in this paper allows a comprehensive quantitative comparison between these phonemic analysis procedures. Given the best available data and the machine-assisted procedures described, there is a strong indication that phonetic similarity is the most important piece of evidence in a phonemic analysis.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 Elsevier B.V.. This is an author produced version of a paper subsequently published in Journal Title. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Phonemic analysis; Endangered languages; Field linguistics |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EPSRC EP/ P502748/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jul 2014 13:10 |
Last Modified: | 18 Jun 2015 17:38 |
Published Version: | http://dx.doi.org/10.1016/j.specom.2013.02.006 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.specom.2013.02.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79642 |