Chapters authored
A Voice Signal Filtering Methods for Speaker Biometric Identification
The preliminary stage of the personality biometric identification on a voice is voice signal filtering. For biometric identification are considered and in number investigated the following methods of noise suppression in a voice signal. The smoothing adaptive linear time filtering (algorithm of the minimum root mean square error, an algorithm of recursive least squares, an algorithm of Kalman filtering, a Lee algorithm), the smoothing adaptive linear frequency filtering (the generalized method, the MLEE (maximum likelihood envelope estimation) method, a wavelet analysis with threshold processing (universal threshold, SURE (Stein’s Unbiased Risk Estimator)-threshold, minimax threshold, FDR (False Discovery Rate)-threshold, Bayesian threshold were used), the smoothing non-adaptive linear time filtering (the arithmetic mean filter, the normalized Gauss’s filter, the normalized binomial filter), the smoothing nonlinear filtering (geometric mean filter, the harmonic mean filter, the contraharmonic filter, the α-trimmed mean filter, the median filter, the rank filter, the midpoint filter, the conservative filter, the morphological filter). Results of a numerical research of denoising methods for voice signals people from the TIMIT (Texas Instruments and Massachusetts Institute of Technology) database which were noise an additive Gaussian noise and multiplicative Gaussian noise were received.
Part of the book: Recent Advances in Biometrics
Methods for Speech Signal Structuring and Extracting Features
The preliminary stage of the biometric identification is speech signal structuring and extracting features. For calculation of the fundamental tone are considered and in number investigated the following methods – autocorrelation function (ACF) method, average magnitude difference function (AMDF) method, simplified inverse filter transformation (SIFT) method, method on a basis a wavelet analysis, method based on the cepstral analysis, harmonic product spectrum (HPS) method. For speech signal extracting features are considered and in number investigated the following methods – the digital bandpass filters bank; spectral analysis; homomorphic processing; linear predictive coding. This methods make it possible to extract linear prediction coefficients (LPC), reflection coefficients (RC), linear prediction cepstral coefficients (LPCC), log area ratio (LAR) coefficients, mel-frequency cepstral coefficients (MFCC), barkfrequency cepstral coefficients (BFCC), perceptual linear prediction coefficients (PLPC), perceptual reflection coefficients (PRC), perceptual linear prediction cepstral coefficients (PLPCC), perceptual log area ratio (PLAR) coefficients, reconsidered perceptual linear prediction coefficients (RPLPC), reconsidered perceptual reflection coefficients (RPRC), reconsidered perceptual linear prediction cepstral coefficients (RPLPCC), reconsidered perceptual log area ratio (RPLAR) coefficients. The largest probability of identification (equal 0.98) and the smallest number of coefficients (4 coefficients) are provided by coding of a vocal of the speech sound from the TIMIT based on PRC.
Part of the book: Computational Semantics