dc.contributor.author	Mazenan, Mohd. Nizam
dc.date.accessioned	2023-08-21T08:04:12Z
dc.date.available	2023-08-21T08:04:12Z
dc.date.issued	2016
dc.description	Thesis (PhD. (Biomedical Engineering))
dc.description.abstract	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
dc.description.sponsorship	Faculty of Biosciences and Medical Engineering
dc.identifier.citation	NA
dc.identifier.issn	NA
dc.identifier.uri	http://openscience.utm.my/handle/123456789/638
dc.language.iso	en
dc.publisher	Universiti Teknologi Malaysia
dc.relation.ispartofseries	NA; NA
dc.subject	Automatic speech recognition
dc.subject	Speech disorders--Research--Evaluation
dc.title	Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
dc.type	Thesis
dc.type	Dataset

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

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