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dc.contributor.authorMartirosian, Olga Meruzhanovna
dc.date.accessioned2011-10-03T14:17:47Z
dc.date.available2011-10-03T14:17:47Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10394/4902
dc.descriptionThesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.
dc.description.abstractThe pronunciation dictionary is a key resource required during the development of an automatic speech recognition (ASR) system. In this thesis, we adapt a British English pronunciation dictionary to Standard South African English (SSAE), as a case study in dialect adaptation. Our investigation leads us in three different directions: dictionary verification, phoneme redundancy evaluation and phoneme adaptation. A pronunciation dictionary should be verified for correctness before its implementation in experiments or applications. However, employing a human to verify a full pronunciation dictionary is an indulgent process which cannot always be accommodated. In our dictionary verification research we attempt to reduce the human effort required in the verification of a pronunciation dictionary by implementing automatic and semi-automatic techniques that find and isolate possible erroneous entries in the dictionary. We identify a number of new techniques that are very efficient in identifying errors, and apply them to a public domain British English pronunciation dictionary. Investigating phoneme redundancy involves looking into the possibility that not all phoneme distinctions are required in SSAE, and investigating different methods of analysing these distinctions. The methods that are investigated include both data driven and knowledge based pronunciation suggestions for a pronunciation dictionary used in an automatic speech recognition (ASR) system. This investigation facilitates a deeper linguistic insight into the pronunciation of phonemes in SSAE. Finally, we investigate phoneme adaptation by adapting the KIT phoneme between two dialects of English through the implementation of a set of adaptation rules. Adaptation rules are extracted from literature but also formulated through an investigation of the linguistic phenomena in the data. We achieve a 93% predictive accuracy, which is significantly higher than the 71 % achievable through the implementation of previously identified rules. The adaptation of a British pronunciation dictionary to SSAE represents the final step of developing a SSAE pronunciation dictionary, which is the aim of this thesis. In addition, an ASR system utilising the dictionary is developed, achieving an unconstrained phoneme accuracy of 79.7%.
dc.language.isoenen_US
dc.publisherNorth-West University
dc.subjectPronunciation dictionariesen_US
dc.subjectPronunciation modellingen_US
dc.subjectDictionary verificationen_US
dc.subjectKIT vowelen_US
dc.subjectDiphthong analysisen_US
dc.subjectSouth African Englishen_US
dc.subjectStandard South African Englishen_US
dc.subjectDialect adaptationen_US
dc.subjectDialect adaptationen_US
dc.subjectBEEP pronunciation dictionaryen_US
dc.subjectCELEX pronunciation dictionaryen_US
dc.titleAdapting a pronunciation dictionary to Standard South African English for automatic speech recognitionen
dc.typeThesisen_US
dc.description.thesistypeMastersen_US


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    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

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