Malay continuous speech recognition using continuous density hidden Markov model

This thesis describes the investigation of the use of Continuous Density Hidden Markov Model (CDHMM) for Malay Automatic Speech Recognition (ASR). The goal of this thesis is to solve the constraints of current Malay ASR that are: speaker-dependent, small vocabulary and isolated words, and provides a...

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Main Author:	Ting, Chee Ming
Format:	Thesis
Language:	English
Published:	2007
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/6426/4/TingCheeMingMFKE2007.pdf
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id	my-utm-ep.6426
record_format	uketd_dc
spelling	my-utm-ep.64262020-07-26T08:11:10Z Malay continuous speech recognition using continuous density hidden Markov model 2007-05 Ting, Chee Ming TK Electrical engineering. Electronics Nuclear engineering This thesis describes the investigation of the use of Continuous Density Hidden Markov Model (CDHMM) for Malay Automatic Speech Recognition (ASR). The goal of this thesis is to solve the constraints of current Malay ASR that are: speaker-dependent, small vocabulary and isolated words, and provides a basis in developing speaker-independent (SI) Malay large vocabulary continuous speech recognition (LVCSR). Hidden Markov Model (HMM) based statistical modeling is used in Malay speech recognition. HMM is a robust and powerful technique capable of modeling of speech signals. With their efficient training algorithm (Baum-Welch and Viterbi/Segmental K-mean) and recognition algorithm (Viterbi), as well as itâ€™s modeling flexibility in model topology, observation probability distribution, representation of speech unit and other knowledge sources, HMM has been successfully applied in solving various tasks in this thesis. CDHMM which model the continuous acoustic space eliminates quantization error imposed by discrete HMM. CDHMM performs better than discrete HMM in Malay speech recognition. CDHMM with mixture densities which is capable to model inter-speaker variability performs well in multi speaker task (99% in isolated words task). The result expects its well performance in SI task in the future. A connected words ASR is developed and evaluated on Malay connected digit task and has achieved reasonably good accuracy with limited training data. Segmental K-mean training procedure is proven to perform better than the manual segmentation. The sub-word unit modeling is attempted in Malay phonetic classification and segmentation on medium vocabulary Malay continuous speech database. Experiments are conducted to investigate different feature set and mixture components. The knowledge of continuous ASR architecture and sub-word unit modeling gained in this work has provided basis for Malay LVCSR. For conclusion, the basic idea of HMM implemented in other language domain can be successfully applied in the Malay language domain as well. 2007-05 Thesis http://eprints.utm.my/id/eprint/6426/ http://eprints.utm.my/id/eprint/6426/4/TingCheeMingMFKE2007.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62313 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ting, Chee Ming Malay continuous speech recognition using continuous density hidden Markov model
description	This thesis describes the investigation of the use of Continuous Density Hidden Markov Model (CDHMM) for Malay Automatic Speech Recognition (ASR). The goal of this thesis is to solve the constraints of current Malay ASR that are: speaker-dependent, small vocabulary and isolated words, and provides a basis in developing speaker-independent (SI) Malay large vocabulary continuous speech recognition (LVCSR). Hidden Markov Model (HMM) based statistical modeling is used in Malay speech recognition. HMM is a robust and powerful technique capable of modeling of speech signals. With their efficient training algorithm (Baum-Welch and Viterbi/Segmental K-mean) and recognition algorithm (Viterbi), as well as itâ€™s modeling flexibility in model topology, observation probability distribution, representation of speech unit and other knowledge sources, HMM has been successfully applied in solving various tasks in this thesis. CDHMM which model the continuous acoustic space eliminates quantization error imposed by discrete HMM. CDHMM performs better than discrete HMM in Malay speech recognition. CDHMM with mixture densities which is capable to model inter-speaker variability performs well in multi speaker task (99% in isolated words task). The result expects its well performance in SI task in the future. A connected words ASR is developed and evaluated on Malay connected digit task and has achieved reasonably good accuracy with limited training data. Segmental K-mean training procedure is proven to perform better than the manual segmentation. The sub-word unit modeling is attempted in Malay phonetic classification and segmentation on medium vocabulary Malay continuous speech database. Experiments are conducted to investigate different feature set and mixture components. The knowledge of continuous ASR architecture and sub-word unit modeling gained in this work has provided basis for Malay LVCSR. For conclusion, the basic idea of HMM implemented in other language domain can be successfully applied in the Malay language domain as well.
format	Thesis
qualification_level	Master's degree
author	Ting, Chee Ming
author_facet	Ting, Chee Ming
author_sort	Ting, Chee Ming
title	Malay continuous speech recognition using continuous density hidden Markov model
title_short	Malay continuous speech recognition using continuous density hidden Markov model
title_full	Malay continuous speech recognition using continuous density hidden Markov model
title_fullStr	Malay continuous speech recognition using continuous density hidden Markov model
title_full_unstemmed	Malay continuous speech recognition using continuous density hidden Markov model
title_sort	malay continuous speech recognition using continuous density hidden markov model
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2007
url	http://eprints.utm.my/id/eprint/6426/4/TingCheeMingMFKE2007.pdf
_version_	1747814657205207040

Malay continuous speech recognition using continuous density hidden Markov model

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