# The Use of Correlation Features in the Problem of Speech Recognition
## 2 Related Works and Presentation and Processing of Speech Messages
The authors of the work [52] were engaged in the recognition of phraseological messages. Algorithms for signal processing under conditions of intense interference have been developed in [52]. However, the proportion of correct recognitions (accuracy) when using the algorithm [52] drops sharply when the noise level exceeds 3 dB. With signals significantly exceeding the noise, the algorithm provides about 95% accuracy (rate of correct recognitions). However, it should be noted that such accuracy was obtained on a compressed set of classes, since only 10 control commands were considered. Processing using different speech command cross-correlation was proposed in [53]. The main idea is that cross-correlation portraits allow us to recognize the boundaries between speech commands. Then correlation analysis is applied. However, this algorithm is extremely slow. The article does not provide a detailed description of the initial data sample, nor does it describe methods for obtaining reference commands. Work [54] recognizes the personality of a speaker by an audio signal, for which frequency signal processing is also used. The work [55] is devoted to the application of machine learning methods for the classification of musical genres. It should be mentioned that there are many methods for time series modeling and its prediction. The methods mentioned can be used with varying degrees of utility in simulating the transmission and reception of radio voice messages. Next, autoregressive models, as well as models of recurrent and convolutional neural networks, will be highlighted. Such models can be used as a tool to obtain additional features. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Pros** & **Cons** \\ \hline Regression models & Simplicity, high level of knowledge & Narrow scope, strong retrainability, the need for combined time series \\ Autoregressive models & Taking into account the internal & Impossibility of describing signals with a complex structure \\ Autoregressive models & connections of the signal, the developed & complex structure \\  & mathematical apparatus & Inability to describe speech signals with \\ Exponential smoothing models & Smoothing out slowly changing signals in noisy environments & high accuracy \\ Models on the most similar pattern & Adequate description of normal data & Impossible to represent heterogeneous data \\ Artificial neural networks & Flexible parameter setting, high accuracy & High computational costs, high model complexity, low stability \\ Markov chains & Simplicity, taking into account the & Too short memory for describing speech signals \\ Classification-regression trees & Good interpretability of results, sufficiently & Strong retrainability of models \\ Genetic algorithm & Wide application, relatively high efficiency & High computational cost \\ Support vector machine & Working with non-linear connections, & Necessity of preprocessing, searching \\ Transfer function & & flexible settings & for parameters \\ Fuzzy logic & Adequate description of complex data & High computational cost, low performance \\  & Universality of approach, & Inability to accurately describe speech \\  & probabilistic estimates & messages, complexity of \\ Doubly stochastic models & Ability to describe inhomogeneous and & mathematical analysis \\  & non-stationary signals & Difficulty in identifying model parameters \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representation and processing of time series. The review shows that despite the existence of many algorithms for working with time series, they all have disadvantages. For example, deep models to ensure high recognition accuracy become very complex. Moreover, in the current state of research, when using intelligent algorithms for processing speech commands, insufficient attention is paid to classical methods of digital signal processing, such as building spectra, estimating the autocorrelation function and others.