Wavelet cesptral coefficients for isolated speech recognition

The study proposes an improved feature extraction method that is called Wavelet Cepstral Coefficients (WCC). In traditional cepstral analysis, the cepstrums are calculated with the use of the Discrete Fourier Transform (DFT). Owing to the fact that the DFT calculation assumes signal stationary betwe...

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Main Authors:	Adam, Tarmizi, Salam, Md Sah, Gunawan, Teddy Surya
Format:	Article
Language:	English
Published:	UAD and IAES 2013
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/32110/ http://irep.iium.edu.my/32110/ http://irep.iium.edu.my/32110/ http://irep.iium.edu.my/32110/1/Adam2013_Telkomnika_Wavelet_Cepstral_Coefficients_for_Isolated_Speech_Recognition.pdf

id	iium-32110
recordtype	eprints
spelling	iium-321102013-10-02T03:47:30Z http://irep.iium.edu.my/32110/ Wavelet cesptral coefficients for isolated speech recognition Adam, Tarmizi Salam, Md Sah Gunawan, Teddy Surya TK Electrical engineering. Electronics Nuclear engineering The study proposes an improved feature extraction method that is called Wavelet Cepstral Coefficients (WCC). In traditional cepstral analysis, the cepstrums are calculated with the use of the Discrete Fourier Transform (DFT). Owing to the fact that the DFT calculation assumes signal stationary between frames which in practice is not quite true, the WCC replaces the DFT block in the traditional cepstrum calculation with the Discrete Wavelet Transform (DWT) hence producing the WCC. To evaluate the proposed WCC, speech recognition task of recognizing the 26 English alphabets were conducted. Comparisons with the traditional Mel-Frequency Cepstral Coefficients (MFCC) are done to further analyze the effectiveness of the WCCs. It is found that the WCCs showed some comparable results when compared to the MFCCs considering the WCCs small vector dimension when compared to the MFCCs. The best recognition was found from WCCs at level 5 of the DWT decomposition with a small difference of 1.19% and 3.21% when compared to the MFCCs for speaker independent and speaker dependent tasks respectively. UAD and IAES 2013-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/32110/1/Adam2013_Telkomnika_Wavelet_Cepstral_Coefficients_for_Isolated_Speech_Recognition.pdf Adam, Tarmizi and Salam, Md Sah and Gunawan, Teddy Surya (2013) Wavelet cesptral coefficients for isolated speech recognition. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11 (5). pp. 2731-2738. ISSN 2302-4046 http://www.iaesjournal.com/online/index.php/TELKOMNIKA/article/view/2510 DOI: 10.11591/telkomnika.v11i5.2510
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Adam, Tarmizi Salam, Md Sah Gunawan, Teddy Surya Wavelet cesptral coefficients for isolated speech recognition
description	The study proposes an improved feature extraction method that is called Wavelet Cepstral Coefficients (WCC). In traditional cepstral analysis, the cepstrums are calculated with the use of the Discrete Fourier Transform (DFT). Owing to the fact that the DFT calculation assumes signal stationary between frames which in practice is not quite true, the WCC replaces the DFT block in the traditional cepstrum calculation with the Discrete Wavelet Transform (DWT) hence producing the WCC. To evaluate the proposed WCC, speech recognition task of recognizing the 26 English alphabets were conducted. Comparisons with the traditional Mel-Frequency Cepstral Coefficients (MFCC) are done to further analyze the effectiveness of the WCCs. It is found that the WCCs showed some comparable results when compared to the MFCCs considering the WCCs small vector dimension when compared to the MFCCs. The best recognition was found from WCCs at level 5 of the DWT decomposition with a small difference of 1.19% and 3.21% when compared to the MFCCs for speaker independent and speaker dependent tasks respectively.
format	Article
author	Adam, Tarmizi Salam, Md Sah Gunawan, Teddy Surya
author_facet	Adam, Tarmizi Salam, Md Sah Gunawan, Teddy Surya
author_sort	Adam, Tarmizi
title	Wavelet cesptral coefficients for isolated speech recognition
title_short	Wavelet cesptral coefficients for isolated speech recognition
title_full	Wavelet cesptral coefficients for isolated speech recognition
title_fullStr	Wavelet cesptral coefficients for isolated speech recognition
title_full_unstemmed	Wavelet cesptral coefficients for isolated speech recognition
title_sort	wavelet cesptral coefficients for isolated speech recognition
publisher	UAD and IAES
publishDate	2013
url	http://irep.iium.edu.my/32110/ http://irep.iium.edu.my/32110/ http://irep.iium.edu.my/32110/ http://irep.iium.edu.my/32110/1/Adam2013_Telkomnika_Wavelet_Cepstral_Coefficients_for_Isolated_Speech_Recognition.pdf
first_indexed	2023-09-18T20:46:20Z
last_indexed	2023-09-18T20:46:20Z
_version_	1777409699638935552

Wavelet cesptral coefficients for isolated speech recognition

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