Authors: Waleed H. Abdulla, Yushi Zhang
Addresses: Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland, New Zealand. ' Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland, New Zealand
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.
Keywords: human biometrics; speaker modelling; speaker recognition; voice recognition; voice biometrics; Gammatone filterbank; independent component analysis; speech feature extraction; noisy speech processing; monaural speech signals; speaker identification.
International Journal of Biometrics, 2010 Vol.2 No.4, pp.330 - 349
Published online: 30 Sep 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article