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Development of real-time embedded system with speech recognition for smart house

The hidden Markov model is used for the acoustic modeling of the speech recognition system. The Continuous Density HMM (CDHMM) which models acoustic observation directly using estimated continuous probability density function (pdf) without VQ, has shown to have higher recognition accuracy than DHMM....

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Main Authors: Yahya, Zuraimi, Sheikh Salleh, Sheikh Hussain, Syed Abu Bakar, Syed Abdul Rahman
Format: Monograph
Language:English
Published: Faculty of Electric Engieering 2007
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/6700/1/74257.pdf
http://eprints.utm.my/id/eprint/6700/
http://www.penerbit.utm.my
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spelling my.utm.67002017-07-25T02:18:25Z http://eprints.utm.my/id/eprint/6700/ Development of real-time embedded system with speech recognition for smart house Yahya, Zuraimi Sheikh Salleh, Sheikh Hussain Syed Abu Bakar, Syed Abdul Rahman TK Electrical engineering. Electronics Nuclear engineering The hidden Markov model is used for the acoustic modeling of the speech recognition system. The Continuous Density HMM (CDHMM) which models acoustic observation directly using estimated continuous probability density function (pdf) without VQ, has shown to have higher recognition accuracy than DHMM. In this paper, CDHMM with Multivariate Gaussian Density is used for speaker dependent (SD) and speaker independent (SI) Malay isolated digit recognition and comparison is made with DHMM. The CDHMM was trained by different algorithms- Baum-Welch (BW), Viterbi (VB) (with segmental k-mean estimation) algorithm and combination of BW and VB then comparison is discussed. The training database consisted of 26 speakers with 5 utterances for each Malay digit. In SD task, another 5 utterances each digit from the same speakers are used for testing. Recognition accuracy of CDHMM with BW, VB training and combination of BW and VB is 99.00%, 98.85% and 98.69% respectively while 96.62% for DHMM. The accuracy for BW and Segmental K-Mean training is comparable, but the latter consumed less computational time. In SI task, 40 speakers, different from the training speakers, with each recorded 2 tokens are used for testing. The CDHMM achieves 85.63% accuracy and outperform DHMM with 8.17% improvement. Faculty of Electric Engieering 2007-05-10 Monograph NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/6700/1/74257.pdf Yahya, Zuraimi and Sheikh Salleh, Sheikh Hussain and Syed Abu Bakar, Syed Abdul Rahman (2007) Development of real-time embedded system with speech recognition for smart house. Project Report. Faculty of Electric Engieering, Skudai, Johor. (Unpublished) http://www.penerbit.utm.my
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Yahya, Zuraimi
Sheikh Salleh, Sheikh Hussain
Syed Abu Bakar, Syed Abdul Rahman
Development of real-time embedded system with speech recognition for smart house
description The hidden Markov model is used for the acoustic modeling of the speech recognition system. The Continuous Density HMM (CDHMM) which models acoustic observation directly using estimated continuous probability density function (pdf) without VQ, has shown to have higher recognition accuracy than DHMM. In this paper, CDHMM with Multivariate Gaussian Density is used for speaker dependent (SD) and speaker independent (SI) Malay isolated digit recognition and comparison is made with DHMM. The CDHMM was trained by different algorithms- Baum-Welch (BW), Viterbi (VB) (with segmental k-mean estimation) algorithm and combination of BW and VB then comparison is discussed. The training database consisted of 26 speakers with 5 utterances for each Malay digit. In SD task, another 5 utterances each digit from the same speakers are used for testing. Recognition accuracy of CDHMM with BW, VB training and combination of BW and VB is 99.00%, 98.85% and 98.69% respectively while 96.62% for DHMM. The accuracy for BW and Segmental K-Mean training is comparable, but the latter consumed less computational time. In SI task, 40 speakers, different from the training speakers, with each recorded 2 tokens are used for testing. The CDHMM achieves 85.63% accuracy and outperform DHMM with 8.17% improvement.
format Monograph
author Yahya, Zuraimi
Sheikh Salleh, Sheikh Hussain
Syed Abu Bakar, Syed Abdul Rahman
author_facet Yahya, Zuraimi
Sheikh Salleh, Sheikh Hussain
Syed Abu Bakar, Syed Abdul Rahman
author_sort Yahya, Zuraimi
title Development of real-time embedded system with speech recognition for smart house
title_short Development of real-time embedded system with speech recognition for smart house
title_full Development of real-time embedded system with speech recognition for smart house
title_fullStr Development of real-time embedded system with speech recognition for smart house
title_full_unstemmed Development of real-time embedded system with speech recognition for smart house
title_sort development of real-time embedded system with speech recognition for smart house
publisher Faculty of Electric Engieering
publishDate 2007
url http://eprints.utm.my/id/eprint/6700/1/74257.pdf
http://eprints.utm.my/id/eprint/6700/
http://www.penerbit.utm.my
_version_ 1643644621932199936
score 13.211869

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