A METHOD FOR IMPROVING SPEECH RECOGNITION EFFICIENCY BASED ON GENETIC OPTIMIZATION OF WAVELET FUNCTIONS - Lavrynenko, Oleksandr (Science-based technologies, 2026)
This article addresses the pressing issue of ensuring high reliability in the operation of speech recognition systems under conditions of noise interference. The scientific novelty of this research lies in the development of a method for synthesizing an optimal adaptive wavelet kernel for the initial layers of convolutional neural networks. Unlike existing approaches, which rely on stochastic weight initialization or the use of strictly deterministic basis functions (such as Meyer, Daubechies, or Simlet wavelets), the authors propose an algorithm for the targeted formation of kernel geometry based on Akima interpolation splines. The central focus of the study is the process of optimizing wavelet morphology, where the mean-squared error of the discrepancy between the amplitude-frequency response of the synthesized filter and the energy spectral portrait of a specific speech signal is chosen as the objective function. To solve the problem of minimizing this function in the multidimensional space of spline parameters, a modified parallel genetic algorithm is applied. The use of evolutionary search allows for effectively overcoming the problem of local extrema, characteristic of non-convex surfaces of objective functions, when searching for optimal ordinates of spline nodal points. The article provides a detailed analysis of the algorithm’s convergence up to the 50th generation and an assessment of computational efficiency depending on the number of processor cores used. The results of comparative modeling are presented, confirming the superiority of adaptive kernels over classical analytical wavelets. In particular, the implementation of an optimal adaptive filter into the structure of a convolutional classifier allowed for an increase in speech recognition accuracy by 15–22% at low signal-to-noise ratios of 5–15 dB. It is shown that, thanks to the use of parallel computing schemes, the system’s adaptation time to a new speaker is reduced to 2.1 seconds, which opens up broad prospects for integrating the method into robust voice control systems for unmanned robotic systems and specialized information and communication networks.