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Title: Machine-assisted phonemic analysis
Author: Kempton, Timothy
ISNI:       0000 0004 2728 5253
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2012
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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. Good computer tools are already available to help in a phonemic analysis, but these primarily provide search and sort database functionality, rather than automated analysis. In computational phonology there have been very few studies on the automated discovery of phonological patterns from surface level data such as narrow phonetic transcriptions or acoustics. This thesis addresses the lack of research in this area. The key scientific question underpinning the work in this thesis is "To what extent can a machine algorithm contribute to the procedures needed for a phonemic analysis?". A secondary question is "What insights does such a quantitative evaluation give about the contribution of each of these procedures to a phonemic analysis?" It is demonstrated that a machine-assisted approach can make a measurable contribution to a phonemic analysis for all the procedures investigated; phonetic similarity, phone recognition & alignment, complementary distribution, and minimal pairs. The evaluation measures introduced in this thesis 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. The tools and techniques developed in this thesis have resulted in tangible benefits to the analysis of two under-resourced languages and it is expected that many more languages will follow.
Supervisor: Moore, R. K. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available