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Voice biometric feature using Gammatone filterbank and ICA

Voice biometric feature using Gammatone filterbank and ICA Voice biometric feature extraction is the core task in developing any speaker identification system. This paper proposes a robust feature extraction technique for the purpose of speaker identification. The technique is based on processing monaural speech signal using human auditory system based Gammatone Filterbank (GTF) and Independent Component Analysis (ICA). The measures used to assess the robustness to additive noises and speaker identification performance are defined and discussed. The kkn the proposed feature is evaluated in real environments under varying noisy conditions. The proposed feature is benchmarked against the commonly used features such as: MFCC, PLCC, and PLP, and it outperforms them in different noisy environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Voice biometric feature using Gammatone filterbank and ICA

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2010.035448
Publisher site
See Article on Publisher Site

Abstract

Voice biometric feature extraction is the core task in developing any speaker identification system. This paper proposes a robust feature extraction technique for the purpose of speaker identification. The technique is based on processing monaural speech signal using human auditory system based Gammatone Filterbank (GTF) and Independent Component Analysis (ICA). The measures used to assess the robustness to additive noises and speaker identification performance are defined and discussed. The kkn the proposed feature is evaluated in real environments under varying noisy conditions. The proposed feature is benchmarked against the commonly used features such as: MFCC, PLCC, and PLP, and it outperforms them in different noisy environments.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2010

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