Method for Biometric Coding of Speech Signals Based on Adaptive Empirical Wavelet Transform

In this research, a biometric speech coding method is developed where empirical wavelet transform is used to extract biometric features of speech signals for voice identification of the speaker. This method differs from existing methods because it uses a set of adaptive bandpass Meyer wavelet filters and Hilbert spectral analysis to determine the instantaneous amplitudes and frequencies of internal empirical modes. This makes it possible to use multiscale wavelet analysis for biometric coding of speech signals based on an adaptive empirical wavelet transform, which increases the efficiency of spectral analysis by 1.2 times or 14 % by separating high-frequency speech oscillations into their low-frequency components, namely internal empirical modes. Also, a biometric method for encoding speech signals based on mel-frequency cepstral coefficients has been improved, which uses the basic principles of adaptive spectral analysis using an empirical wavelet transform, which also significantly improves the separation of the Fourier spectrum into adaptive bands of the corresponding formant frequencies of the speech signal.