Wavelet cepstral coefficients for isolated speech recognition

Wavelet cepstral 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, Muhammad, Gunawan, Teddy Surya
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://irep.iium.edu.my/27203/
http://irep.iium.edu.my/27203/
http://irep.iium.edu.my/27203/1/ICIDM_2012.pdf
id iium-27203
recordtype eprints
spelling iium-272032013-01-04T06:54:30Z http://irep.iium.edu.my/27203/ Wavelet cepstral coefficients for isolated speech recognition Adam, Tarmizi Salam, Muhammad 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. Comparison 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. 2012-12-03 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/27203/1/ICIDM_2012.pdf Adam, Tarmizi and Salam, Muhammad and Gunawan, Teddy Surya (2012) Wavelet cepstral coefficients for isolated speech recognition. In: International Conference on Interactive Digital Media, 3-4 December 2012, Bayview Hotel, Langkawi. (In Press) http://seminar.utmspace.utm.my/ICIDM2012/index.html
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, Muhammad
Gunawan, Teddy Surya
Wavelet cepstral 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. Comparison 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 Conference or Workshop Item
author Adam, Tarmizi
Salam, Muhammad
Gunawan, Teddy Surya
author_facet Adam, Tarmizi
Salam, Muhammad
Gunawan, Teddy Surya
author_sort Adam, Tarmizi
title Wavelet cepstral coefficients for isolated speech recognition
title_short Wavelet cepstral coefficients for isolated speech recognition
title_full Wavelet cepstral coefficients for isolated speech recognition
title_fullStr Wavelet cepstral coefficients for isolated speech recognition
title_full_unstemmed Wavelet cepstral coefficients for isolated speech recognition
title_sort wavelet cepstral coefficients for isolated speech recognition
publishDate 2012
url http://irep.iium.edu.my/27203/
http://irep.iium.edu.my/27203/
http://irep.iium.edu.my/27203/1/ICIDM_2012.pdf
first_indexed 2023-09-18T20:40:27Z
last_indexed 2023-09-18T20:40:27Z
_version_ 1777409329880629248

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