Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu

Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labe...

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Main Author:	Nik Nurhidayat, Nik Him
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	TK7885-7895 Computer engineering Computer hardware
Online Access:	https://etd.uum.edu.my/5276/1/s814595.pdf https://etd.uum.edu.my/5276/2/s814595_abstract.pdf
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id	my-uum-etd.5276
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Husni, Husniza
topic	TK7885-7895 Computer engineering Computer hardware
spellingShingle	TK7885-7895 Computer engineering Computer hardware Nik Nurhidayat, Nik Him Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu
description	Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labelling of dyslexic children‟s reading in BM. For that, three objectives have been set: first to produce manual transcription and phonetic labelling; second to construct automatic transcription and phonetic labelling using forced alignment; and third to compare between accuracy using automatic transcription and phonetic labelling and manual transcription and phonetic labelling. Therefore, to accomplish these goals methods have been used including manual speech labelling and segmentation, forced alignment, Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for training, and for measure accuracy of ASR, Word Error Rate (WER) and False Alarm Rate (FAR) were used. A number of 585 speech files are used for manual transcription, forced alignment and training experiment. The recognition ASR engine using automatic transcription and phonetic labelling obtained optimum results is 76.04% with WER as low as 23.96% and FAR is 17.9%. These results are almost similar with ASR engine using manual transcription namely 76.26%, WER as low as 23.97% and FAR a 17.9%. As conclusion, the accuracy of automatic transcription and phonetic labelling is acceptable to use it for help dyslexic children learning using ASR in Bahasa Melayu (BM)
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Nik Nurhidayat, Nik Him
author_facet	Nik Nurhidayat, Nik Him
author_sort	Nik Nurhidayat, Nik Him
title	Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu
title_short	Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu
title_full	Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu
title_fullStr	Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu
title_full_unstemmed	Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu
title_sort	automatic transcription and phonetic labelling of dyslexic children's reading in bahasa melayu
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5276/1/s814595.pdf https://etd.uum.edu.my/5276/2/s814595_abstract.pdf
_version_	1747827898083966976
spelling	my-uum-etd.52762021-04-04T08:08:01Z Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu 2015 Nik Nurhidayat, Nik Him Husni, Husniza Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences TK7885-7895 Computer engineering. Computer hardware Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labelling of dyslexic children‟s reading in BM. For that, three objectives have been set: first to produce manual transcription and phonetic labelling; second to construct automatic transcription and phonetic labelling using forced alignment; and third to compare between accuracy using automatic transcription and phonetic labelling and manual transcription and phonetic labelling. Therefore, to accomplish these goals methods have been used including manual speech labelling and segmentation, forced alignment, Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for training, and for measure accuracy of ASR, Word Error Rate (WER) and False Alarm Rate (FAR) were used. A number of 585 speech files are used for manual transcription, forced alignment and training experiment. The recognition ASR engine using automatic transcription and phonetic labelling obtained optimum results is 76.04% with WER as low as 23.96% and FAR is 17.9%. These results are almost similar with ASR engine using manual transcription namely 76.26%, WER as low as 23.97% and FAR a 17.9%. As conclusion, the accuracy of automatic transcription and phonetic labelling is acceptable to use it for help dyslexic children learning using ASR in Bahasa Melayu (BM) 2015 Thesis https://etd.uum.edu.my/5276/ https://etd.uum.edu.my/5276/1/s814595.pdf text eng public https://etd.uum.edu.my/5276/2/s814595_abstract.pdf text eng public masters masters Universiti Utara Malaysia Abushariah, A. A. M., Gunawan, T. S., Khalifa, O. O., Abushariah, M. A. M. (2010). English digits speech recognition system based on Hidden Markov Models. In International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia. Al-Manie, M. A., Alkanhal, M. I., & Al-Ghamdi, M. M. (2009). Automatic speech segmentation using the Arabic phonetic database. 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