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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/27203/ http://irep.iium.edu.my/27203/ http://irep.iium.edu.my/27203/1/ICIDM_2012.pdf |
| Summary: | 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. |
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