tailieunhanh - Báo cáo hóa học: " Research Article On the Use of Complementary Spectral Features for Speaker Recognition"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article On the Use of Complementary Spectral Features for Speaker Recognition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 258184 10 pages doi 2008 258184 Research Article On the Use of Complementary Spectral Features for Speaker Recognition Danoush Hosseinzadeh and Sridhar Krishnan Department of Electrical and Computer Engineering Ryerson University 350 Victoria Street Toronto ON Canada M5B 2K3 Correspondence should be addressed to Sridhar Krishnan krishnan@ Received 29 November 2006 Revised 7 May 2007 Accepted 29 September 2007 Recommended by Tan Lee The most popular features for speaker recognition are Mel frequency cepstral coefficients MFCCs and linear prediction cepstral coefficients LPCCs . These features are used extensively because they characterize the vocal tract configuration which is known to be highly speaker-dependent. In this work several features are introduced that can characterize the vocal system in order to complement the traditional features and produce better speaker recognition models. The spectral centroid SC spectral bandwidth SBW spectral band energy SBE spectral crest factor SCF spectral flatness measure SFM Shannon entropy SE and Renyi entropy RE were utilized for this purpose. This work demonstrates that these features are robust in noisy conditions by simulating some common distortions that are found in the speakers environment and a typical telephone channel. Babble noise additive white Gaussian noise AWGN and a bandpass channel with 1 dB of ripple were used to simulate these noisy conditions. The results show significant improvements in classification performance for all noise conditions when these features were used to complement the MFCC and AMFCC features. In particular the SC and SCF improved performance in almost all noise conditions within the examined SNR range 10-40 dB . For example in cases where there was only one source of distortion classification improvements of up to 8 and 10 were achieved under babble noise and AWGN

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