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Download e-book for kindle: Advanced Topics In Biometrics by Haizhou Li, Kar-Ann Toh, Liyuan Li

By Haizhou Li, Kar-Ann Toh, Liyuan Li

ISBN-10: 9814287849

ISBN-13: 9789814287845

Biometrics is the research of equipment for uniquely spotting people in line with a number of intrinsic actual or behavioral features. After many years of analysis actions, biometrics, as a famous medical self-discipline, has complicated significantly either in sensible know-how and theoretical discovery to satisfy the expanding desire of biometric deployments. during this ebook, the editors offer either a concise and obtainable advent to the sector in addition to an in depth insurance at the distinct learn issues of their recommendations in a large spectrum of biometrics study starting from voice, face, fingerprint, iris, handwriting, human habit to multimodal biometrics. The contributions additionally current the pioneering efforts and state of the art effects, with certain specialize in functional matters relating process improvement. This booklet is a useful reference for tested researchers and it additionally offers a superb creation for newbies to appreciate the demanding situations.

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Lee, K. , and Kinnunen, T. A GMM-based probabilistic sequence kernel for speaker verification, in Proc. Interspeech, pp. 294–297, 2007. Leggetter, C. J. and Woodland, P. C. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models, Computer Speech and Language 9, 171–185, 1995. , Lee, K. , Sim, K. , and Jin, Q. The 14U system in NIST 2008 speaker recognition evaluation, in Proc. ICASSP, pp. 4201–4204, 2009. Li, K. P. and Porter, J. E. Normalizations and selection of speech segments for speaker recognition scoring, in Proc.

ICASSP, pp. 1:595–598, 1988. , and Dai, L. Exploiting prosodic information for speaker recognition, in Proc. ICASSP, pp. 4225–4228, 2009. Lucey, S. and Chen, T. Improved speaker verification through probabilistic subspace adaptation, in Proc. Eurospeech, pp. 2021–2024, 2003. , and Li, H. Speaker cluster based GMM tokenization for speaker recognition, in Proc. Interspeech, pp. 505–508, 2006. Matsui, T. and Furui, S. Concatenated phoneme models for text-variable speaker recognition, in Proc. ICASSP, pp.

K} and Cbl = {bl , l = 1, . . , L} are constructed by clustering the transformation components for the parameter sharing. The objection function that trains the feature transforms based on the MAP criterion is defined as K L p(Ak ) L(Θ, λ) = p(F(x; Θ)|λ) k=1 p(bl ) l=1 where p(Ak ) and p(bl ) are the prior PDF of Ak and bl , respectively. 5in 18 Advanced Topics in Biometrics b1057-ch01 B. Ma and H. Li The FT technique rides on the GMM–UBM framework. Unlike the MLLR transform, it does not require speech transcripts to estimate the transformation matrices.

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Advanced Topics In Biometrics by Haizhou Li, Kar-Ann Toh, Liyuan Li

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