Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource]

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dc.contributor.advisorMbogho, Audrey J Wen_ZA
dc.contributor.authorIdowu, Modupeola Florenceen_ZA
dc.date.accessioned2014-11-03T08:32:22Z
dc.date.available2014-11-03T08:32:22Z
dc.date.issued2011en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractAn automatic speech recognition (ASR) system is a software application which recognizes human speech, processes it as input, and displays a text version of the speech as output or uses the input as commands for another application’s usage. ASR can either be speaker-dependent or speakerindependent. A speaker-dependent ASR system requires every user to perform training before its usage, while speaker-independent ASR requires no prior training before usage. The technology of ASR is based on identification and comparison of sound patterns; these sound patterns are combinations of the smallest units of sound called phonemes. The phonemes constitute fragments of uttered sounds in speech and their combination gives meaningful sound patterns in languages. There exists a set of phonemes for every language group, and associated with each group is the method of pronunciation called the accent. A language group could be identified by the accent in their speech; accent is the set of pronunciation rules of a language group. Accent reflects the cultural divide of a multi cultural society with a common language such as English. Some commercially available ASR systems are designed based on the accents of the following language groups: English, French, German, Italian, Dutch, and Spanish. These language groups are European with none having any similarities with African languages and accents, (except Afrikaans and English, which, though spoken in Africa, originated from Proto-Indo-European languages). This study involved the evaluation of commercially available English ASR systems, establishing their usability and usefulness among different language groups in South Africa which use English as a common language. Of particular interest was the effect of African accents on the performance of the ASR systems. ASR technology is widely used and researched in the developed world with reported recognition accuracy of up to 99%. However, English spoken with African accents may have adverse effect on the recognition accuracy. Despite the fact that most existing ASR systems are not designed for English spoken with South Africans’ accents, one can easily purchase them over the shelf in South Africa. The systems used in this study are: 1. Nuance Dragon NaturallySpeaking, Version10.0 (NDNS). 2. Windows Speech Recognition, Windows Vista version (WSR). The result of this study indicated that accent has influence on the ASR recognition accuracy. It also indicated that users’ satisfaction was greatly affected by the recognition accuracy obtained. The results also indicated poor performance in environments where speech cannot be loud, for example, in the library.en_ZA
dc.identifier.apacitationIdowu, M. F. (2011). <i>Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource]</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/9042en_ZA
dc.identifier.chicagocitationIdowu, Modupeola Florence. <i>"Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource]."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2011. http://hdl.handle.net/11427/9042en_ZA
dc.identifier.citationIdowu, M. 2011. Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource]. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Idowu, Modupeola Florence AB - An automatic speech recognition (ASR) system is a software application which recognizes human speech, processes it as input, and displays a text version of the speech as output or uses the input as commands for another application’s usage. ASR can either be speaker-dependent or speakerindependent. A speaker-dependent ASR system requires every user to perform training before its usage, while speaker-independent ASR requires no prior training before usage. The technology of ASR is based on identification and comparison of sound patterns; these sound patterns are combinations of the smallest units of sound called phonemes. The phonemes constitute fragments of uttered sounds in speech and their combination gives meaningful sound patterns in languages. There exists a set of phonemes for every language group, and associated with each group is the method of pronunciation called the accent. A language group could be identified by the accent in their speech; accent is the set of pronunciation rules of a language group. Accent reflects the cultural divide of a multi cultural society with a common language such as English. Some commercially available ASR systems are designed based on the accents of the following language groups: English, French, German, Italian, Dutch, and Spanish. These language groups are European with none having any similarities with African languages and accents, (except Afrikaans and English, which, though spoken in Africa, originated from Proto-Indo-European languages). This study involved the evaluation of commercially available English ASR systems, establishing their usability and usefulness among different language groups in South Africa which use English as a common language. Of particular interest was the effect of African accents on the performance of the ASR systems. ASR technology is widely used and researched in the developed world with reported recognition accuracy of up to 99%. However, English spoken with African accents may have adverse effect on the recognition accuracy. Despite the fact that most existing ASR systems are not designed for English spoken with South Africans’ accents, one can easily purchase them over the shelf in South Africa. The systems used in this study are: 1. Nuance Dragon NaturallySpeaking, Version10.0 (NDNS). 2. Windows Speech Recognition, Windows Vista version (WSR). The result of this study indicated that accent has influence on the ASR recognition accuracy. It also indicated that users’ satisfaction was greatly affected by the recognition accuracy obtained. The results also indicated poor performance in environments where speech cannot be loud, for example, in the library. DA - 2011 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2011 T1 - Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource] TI - Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource] UR - http://hdl.handle.net/11427/9042 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9042
dc.identifier.vancouvercitationIdowu MF. Evaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource]. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2011 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9042en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleEvaluation of the usability and usefulness of automatic speech recognition among users in South Africa [electronic resource]en_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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