# The Use of Correlation Features in the Problem of Speech Recognition
## 2 Related Works and Presentation and Processing of Speech Messages  
Speech recognition is a traditional classification task, where there is a limited set of sounds or phonemes. From this point of view, its solution is possible using classification models, as, for example, deep learning, machine learning or rule-based methods. Articles for processing Arabic messages [18; 19; 20] provide the main ideas for speech signal classification. The task is greatly simplified by reducing the number of classes. This can happen when it is only necessary to determine an emotion [21] or a spectrum of emotions [22] from a voice message. Recognition of speech messages in Russian is considered in [23; 24; 25]. However, sound signal processing approaches also consist in the selection of phonemes in a temporal signal [26]. The rule-based phoneme detection approach developed by the authors of [26] allowed them to obtain recognition accuracy above 99%. In [27], methods of working with hierarchical phonemes are already used. This allows authors to filter out non-existent or unlikely combinations of sounds and generally improves the quality of recognition. Methods based on attention are considered in work [28]. At the same time, it was possible to improve the quality of recognition by 5-6%, focusing on vowels. However, it should be noted that all the presented algorithms operate with pure signals. When it comes to noisy expressions, recognition becomes much more difficult [29; 30; 31]. In particular, in [29], the recognition accuracy is already only 91%. Work [30] shows that it is important to apply frequency area processing methods to work with noisy messages. In particular, the authors of [29] use the Fourier transform and also use the Gilbert transform for analysis. The training set augmentation approach [31; 32] also proves to be effective in the absence of noisy data in the training set. The work [32] is devoted to the augmentation of noisy images, and the approaches considered in [32] can be applied to autocorrelation portraits of voice messages. Since the recording of a voice message is signal amplitude changing in dependence on time, then time series models are adequate tools for representing voice messages. Traditional methods for time series modeling and processing are covered by the work [33]. Nevertheless, this work is of a fundamental nature, without application to the problems of analyzing speech messages. Stochastic models are the class of the most popular models used in the time series representation. Next, we consider the classification of methods for time series modeling and its processing. Models for time series prediction are based on regression [34]. These are models entirely based on regression analysis. The main advantage of such models are sufficient simplicity and a high degree of knowledge, but the main disadvantages of this approach are the insufficiency of such models, excessive overfitting and the inability to capture nonlinear relationships without additional data preprocessing. Autoregressive models, e.g., Autoregressive Integrated Moving Average eXtended (ARIMAX), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) [35] or Autoregressive Distributed Lag Model (ARDLM) [36] are better suited for representing time series because such models consider relationships within a changing signal time discrete period. With regard to phraseological radio exchange messages, it is also possible to analyze the state of the next signal level based on the previous ones. For the GARCH model, there are quite a few modifications applicable to certain specific tasks.