Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm

Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is s...

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Main Author:	Mazenan, Mohd. Nizam
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QH301 Biology
Online Access:	http://eprints.utm.my/id/eprint/77747/1/MohdNizamMazenanPFBME2016.pdf
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spelling	my-utm-ep.777472018-07-04T11:42:55Z Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm 2015-10 Mazenan, Mohd. Nizam QH301 Biology Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task. 2015-10 Thesis http://eprints.utm.my/id/eprint/77747/ http://eprints.utm.my/id/eprint/77747/1/MohdNizamMazenanPFBME2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:97406 phd doctoral Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering Faculty of Biosciences and Medical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QH301 Biology
spellingShingle	QH301 Biology Mazenan, Mohd. Nizam Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
description	Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mazenan, Mohd. Nizam
author_facet	Mazenan, Mohd. Nizam
author_sort	Mazenan, Mohd. Nizam
title	Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
title_short	Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
title_full	Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
title_fullStr	Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
title_full_unstemmed	Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
title_sort	malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering
granting_department	Faculty of Biosciences and Medical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/77747/1/MohdNizamMazenanPFBME2016.pdf
_version_	1747817821052600320

Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm

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