NDLI: Using predictive differential power spectrum and subband mel-spectrum centroid for robust speaker recognition in stationary noises

Content Provider	IEEE Xplore Digital Library
Author	Jing Deng Zheng, Thomas Fang Zhan-Jiang Song Jian Liu Wen-Hu Wu
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China (Jing Deng; Thomas Fang Zheng)
Abstract	In state-of-the-art speaker recognition systems, mel-scaled frequency cepstral coefficients (MFCCs) are perhaps the most widely used front-ends. One of the major issues with the MFCCs is that they are very sensitive to additive noises. In this paper, two methods for robust speech front-ends are proposed. One is to use a predictive difference function to calculate the differential power spectrums (DPS) as precisely as possible in order to restore the power spectrum of its original clean speech. The spectrum in the traditional MFCC calculation is then replaced with this estimated spectrum and the extracted features based on this are referred to as predictive differential power spectrum (PDPS) based cepstral coefficients (PDPSCCs). The other is to incorporate subband power information with subband mel-spectrum centroid information after the outputs of traditional mel-filter banks. The extracted features based on this are referred to as subband mel-spectrum centroid (SMSC) based cepstral coefficients (SMSCCCs). PDPSCCs and SMSCCCs with cepstral mean subtraction (CMS) based, spectral subtraction (SS) based, and differential power spectrum (DPS) based cepstral coefficients are compared at different noise levels. Experimental results show that the PDPSCCs and SMSCCCs are more effective in enhancing the robustness of a speaker recognition system, where with the CMS method the average error rate can be reduced by 12.2% in comparison with DPS based cepstral coefficients.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4846
Ending Page	4851
File Size	466470
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Speaker recognition Cepstral analysis Mel frequency cepstral coefficient Speech Data mining Feature extraction Collision mitigation Additive noise Noise level subband Robust speaker recognition difference function
Content Type	Text
Resource Type	Article

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