By Haizhou Li, Kar-Ann Toh, Liyuan Li
Biometrics is the learn of equipment for uniquely spotting people in keeping with a number of intrinsic actual or behavioral characteristics. After many years of analysis actions, biometrics, as a famous medical self-discipline, has complicated significantly either in functional expertise and theoretical discovery to fulfill the expanding desire of biometric deployments. during this ebook, the editors offer either a concise and available advent to the sector in addition to a close insurance at the special study issues of their strategies in a large spectrum of biometrics learn starting from voice, face, fingerprint, iris, handwriting, human habit to multimodal biometrics. The contributions additionally current the pioneering efforts and cutting-edge effects, with distinctive concentrate on sensible matters bearing on process improvement. This e-book is a worthy reference for verified researchers and it additionally provides an outstanding creation for novices to appreciate the demanding situations.
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Additional resources for Advanced Topics In Biometrics
The phonetic symbols mainly follow the ARPABET convention. The distinctive features that describe a segment can be classiﬁed into three types: articulator-free features, articulator features, and articulator-bound features. 1 Articulator-free features Articulator-free features describe the type of sound being produced — vowels, glides, consonants, and the particular types of consonants. 5in Advanced Topics in Biometrics Distinctive Features in the Representation of Speech and Acoustic Correlates b1057-ch02 35 features: vowel, glide, consonant, sonorant, continuant, and strident.
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And Mermelstein, P. Comparison of parametric representation for monosyllabic word recognition in continuous spoken sentence, IEEE Trans. Acoustic and Speech Signal Processing 28, 357–366, 1980. , and Neumeyer, L. G. Speaker adaptation using constrained estimation of Gaussian mixtures, IEEE Trans. Speech and Audio Processing 3, 5, 357–366, 1995. Doddington, G. Speaker recognition based on idiolectal diﬀerences between speakers, in Proc. Eurospeech, pp. 2521–2524, 2001. , and Rubin, D. Maximum likelihood from incomplete data via the EM algorithm, J.