METHOD FOR OBTAINING VECTORS OF ACOUSTIC CHARACTERISTICS FOR PHRASE SEQUENCE RECOGNITION IN CONDITIONS OF NOISE INTERFERENCE
Background. Improvement of the accuracy of speech recognition under noise interference is an important task, because there are applications of speech recognition systems, where, despite the growing relevance, there is a problem of high recognition accuracy achievement in the presence of various noise impacts. The research object is on-board systems of automatic speech recognition. The research subject is the improvement of speech recognition probability under noise interference. The work purpose is to develop methods and algorithms of the recognition accuracy improvement under noise interference. Materials and methods. The research of speech recognition methods under noise interference was implemented using neural networks and hidden Markov models. Results. The authors have developed a method of obtaining a vector of acoustic features based on the use of chalk-frequency cepstral coefficients. The basis of this method of training is formed by a new formula of linear single-layer neural networks (LONS), obtained through the use of two target functions. The first objective function - a function of the probability of the normal Gaussian multivariate distribution, the second objective function - a function of calculating the cepstral coefficients based on the use of LONS for computing the average spectral power. Conclusions. The authors have suggested the method of obtaining a vector of acoustic features based on the use of chalk-frequency cepstral coefficients. The LONS learning algorithm, which is based on the formula of training obtained through the application of the two objective functions, enhanced the probability of speech recognition under noise interference.