# The Use of Correlation Features in the Problem of Speech Recognition
## 2 Related Works and Presentation and Processing of Speech Messages
GA is used in robotics, in the development of computer games. It can also be useful for optimizing database operations, for choosing optimal routes, and even for neural network training. In [45], the GA model is used to time series predicting. The model based on GA also requires a large number of calculations and is hardly applicable to the problem of describing speech messages of phraseological radio exchange. Support Vector Machine (SVM) is a widely known machine learning algorithm. As with a decision tree, SVM can be used to predict values (regression task) and classify data (classification task). The method builds a non-linear separating plane and can be more efficient than linear models in some cases. At the same time, SVM provides high flexibility in data description [46]. Among the disadvantages, it is worth singling out the need for additional data preprocessing before training. Another weakness is the fact that algorithm often requires the parameters selection using a highly computational grid search method. It should also be noted that this method can be applied for predicting speech messages only after normalization. Some applications use a model based on transfer functions (TF) for time series processing. The TF model also uses artificial neural network algorithms [47]. The absolute advantage of this approach is the ability to describe complex systems and relationships between data. However, training requires significant computational costs, and at the same time, the greater the complexity of the model, the longer it will take to display the results. In terms of performance, the model is significantly inferior to autoregressions. Fuzzy logic (FL) systems are suggested to make fuzzy predictions. The theory of fuzzy sets has also proven itself in various applied tasks. Estimating the probabilities of belonging to a class in neural networks resembles fuzzy logic models in terms of the principle of operation. Such models solve problems of complex technical systems analysis [48]. At the same time, since such logic claims to be broad and universal, the solution of the narrow problem of predicting a time series from a speech message is likely to be suboptimal. Other disadvantages can be, for example, the lack of standard methodology for constructing fuzzy systems, the impossibility of fuzzy systems mathematical analysis using existing methods, and the fact that the use of a fuzzy approach compared to a probabilistic one does not lead to an increase in the accuracy of calculations. Doubly stochastic models (DSM) are extended version of the stochastic models. In 1987, [PERSON] et al. [49] propose models whose parameters are random. It provided the property of being doubly stochastic and made it possible to form an apparatus for modeling non-stationary signals, which is quite beneficial in describing speech messages. The difficulties of identifying the internal and external parameters of models are the main disadvantage for high-order models; however, preferable results were also obtained for time series processing [50; 51]. Summing up the review of algorithms for representing and processing time series, we will compile a comparative table. Table 1 presents the advantages and disadvantages of various approaches. Note that the choice of simpler models is associated with the potential to ensure high processing speed on the onboard equipment. However, for comparison, it is also necessary to study neural network algorithms. Furthermore, none of the presented approaches pay attention to preprocessing algorithms, which will be discussed in our algorithm. If we consider in more detail the processing and classification of speech messages of radio traffic, it should be noted that distortions can be both additive and multiplicative. For example, radio channel interference has a Rayleigh or Rayleigh-Rice distribution. However, it is allowed to approximate the totality of all noise affecting the signal by white Gaussian noise in signal modeling.