# The Use of Correlation Features in the Problem of Speech Recognition
## 2 Related Works and Presentation and Processing of Speech Messages
Among these modifications, the Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH), Non-Symmetric Generalized Autoregressive Conditional Heteroskedasticity (NGARCH), Integrated Generalized Autoregressive Conditional Heteroskedasticity (IGARCH), Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and Generalized Autoregressive Conditional Heteroskedasticity in Mean (GARCH-M) models should be highlighted. The undoubted advantage of using autoregressive models is a well-developed mathematical apparatus, existing signal processing algorithms that can be imitated by such models. At the same time, the prediction by autoregressive models is not difficult. Revealing a connection within a signal can be used as an additional feature. The limited use of such models is associated with the impossibility of an adequate description of signals with a complex structure. Models of exponential smoothing (ES) are more tools for time series representation and processing. A detailed description of such models' application is presented in [37]. They are suitable for describing smooth changes in signals, and often allow us to get rid of noise emissions. However, if the original signal has the sharp changes in amplitude, then such models will be of little use. It should be noted that sharp magnitude changes property is quite typical characteristic of phraseological radio exchange signals. Models on the most similar pattern (MMSP) are presented, for example, in the article [38]. For data that follow a normal distribution, this may be a good tool for representation. However, testing this model on non-homogeneous time series shows that the quality of predictions is very low. Artificial Neural Networks (ANN) should be singled out separately as a learning-based data processing model. This is one of the main trends in the processing of any data, and time series is no exception. ANN time series predicting models are presented in [39]. The possibilities of multitasking processing and the big probability (close to 1) of obtaining high accuracy with a sufficient number of training examples make such models promising. The disadvantages are the need for training and high computational costs. To analyze speech messages, it is useful to apply recurrent neural networks (RNN) [40]. The idea of using RNN is that in addition to the new inputs in neural network. So the recurrent network also processes the output of the hidden layer in the previous iteration at the current iteration. Thus, there is a recursion. In addition, for the analysis of speech signals, convolutional neural networks (CNN) have gained wide popularity, but CNNs have proven themselves in image processing [41; 42]. Markov chains [43] are most often used to describe transition states and changes in a random process. For the task of describing speech messages, the use of Markov processes with short memory is likely to be insufficient. However, the advantage of Markov chains is the ability to study time series in dynamics, as well as their simplicity. However, such models do not claim to be universal, especially in terms of evaluating complex radio messages and superimposed interference. Classification and regression trees (CART) is machine learning model with a high level of interpretation. An important property of such a model is that it can also solve the problem of classification (or recognition). Therefore, no additional transformations are required to solve the problem of speech message recognition under study. In [44], a predicting method based on the CART decision tree is considered. The tree itself has a binary structure, which makes it easier to interpret the results. With a given tree depth, it is possible to achieve absolute accuracy on the training data, but in this case, there will be an effect of the model overfitting. Models based on genetic algorithm (GA) can be used for time series processing. Nowadays, the application of GA can be observed in various fields of science and technology.