Analysis of Speech Signal Processing Methods in Speech Recognition Systems

This work provides an overview of modern methods for processing speech signals used in speech recognition systems. Processing methods in the time, frequency, and frequency-time domains have been analyzed. Key approaches such as Fourier transform, wavelet transform, cepstral analysis, linear prediction methods, correlation analysis, using neural network, hidden Markov models, and dynamic time warping are examined. The conducted research allows for an assessment of the capabilities of existing methods, their effectiveness, as well as promising directions for the implementation of new mathematical apparatuses.

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