A METHOD FOR OPTIMIZING THE BASIC WAVELET FUNCTION BY THE CRITERION OF MINIMIZING THE ERROR OF APPROXIMATION OF SPEECH SIGNALS - Lavrynenko, Oleksandr (Science-based technologies, 2025)
The paper presents a developed method for selecting adaptive wavelet features for speech signal recognition based on optimizing the base wavelet function by the criterion of minimizing the approximation error. To build the base wavelet function, it was proposed to use a genetic algorithm, in which the accuracy of speech signal classification by the support vector method is the objective function. The main advantage of the constructed adaptive wavelet function using the proposed method is the fact that it takes into account the dynamics of the speech signal throughout time, since a wavelet function is built at each scale that depends on the entire signal and contains information about all its changes, which directly affects the improvement of the resolution of wavelet features of speech recognition. The analysis of speech signals by the proposed method will consist of the following steps: 1) determination of the speech signal metaclass; 2) selection of the optimal pair “adaptive wavelet function - classifier”; 3) wavelet analysis of the speech signal using the constructed adaptive wavelet function; 4) final classification of adaptive wavelet features for speech signal recognition. It is shown that the use of the basic wavelet functions generated on the basis of the presented method for the selection of adaptive wavelet features for speech signal recognition allows to obtain a steady increase in classification accuracy. In particular, in comparison with the mel-frequency capstral coefficients, the accuracy improvement ranges from 4.1 to 8.3 % for different phonemes of the Ukrainian language. It has been shown that the convolved adaptive wavelet function has the property of preserving information about dynamic processes in the speech signal, and, as a result, the dimensionality of adaptive wavelet recognition features is reduced by 1.5 times without loss of classification accuracy. It is shown that the use of the proposed method allows achieving better speech phoneme classification characteristics than using the classical analysis based on mel-frequency cepstral coefficients by a factor of 1.4.