A method for extracting the semantic features of speech signal recognition based on empirical wavelet transform

The subject of this study is methods for improving the efficiency of semantic coding of speech signals. The purpose of this study is to develop a method for improving the efficiency of semantic coding of speech signals. Coding efficiency refers to the reduction of the information transmission rate with a given probability of error-free recognition of semantic features of speech signals, which will significantly reduce the required source bandwidth, thereby increasing the communication channel bandwidth. To achieve this goal, it is necessary to solve the following scientific tasks: (1) to investigate a known method for improving the efficiency of semantic coding of speech signals based on mel-frequency cepstral coefficients; (2) to substantiate the effectiveness of using the adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals; (3) to develop a method of semantic coding of speech signals based on adaptive empirical wavelet transform with further application of Hilbert spectral analysis and optimal thresholding; and (4) to perform an objective quantitative assessment of the increase in the efficiency of the developed method of semantic coding of speech signals in contrast to the existing method. The following scientific results were obtained during the study: a method of semantic coding of speech signals based on empirical wavelet transform is developed for the first time, which differs from existing methods by constructing a set of adaptive bandpass Meyer wavelet filters with further application of Hilbert spectral analysis to find the instantaneous amplitudes and frequencies of the functions of internal empirical modes, which will allow the identification of semantic features of speech signals and increase the efficiency of their coding; for the first time, it is proposed to use the method of adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals, which will increase the efficiency of spectral analysis by decomposing the high-frequency speech oscillation into its low-frequency components, namely internal empirical modes; the method of semantic coding of speech signals based on mel-frequency cepstral coefficients was further developed, but using the basic principles of adaptive spectral analysis with the help of empirical wavelet transform, which increases the efficiency of this method. Conclusions: We developed a method for semantic coding of speech signals based on empirical wavelet transform, which reduces the encoding rate from 320 to 192 bps and the required bandwidth from 40 to 24 Hz with a probability of error-free recognition of approximately 0.96 (96%) and a signal-to-noise ratio of 48 dB, according to which its efficiency is increased by 1.6 times as compared to the existing method. We developed an algorithm for semantic coding of speech signals based on empirical wavelet transform and its software implementation in the MATLAB R2022b programing language.