# The Use of Correlation Features in the Problem of Speech Recognition
###### Abstract  
The problem solved in the article is connected with the increase in the efficiency of phraseological radio exchange message recognition, which sometimes takes place in conditions of increased tension for the pilot. For high-quality recognition, signal preprocessing methods are needed. The article considers new data preprocessing algorithms used to extract features from a speech message. In this case, two approaches were proposed. The first approach is building autocorrelation functions of messages based on the Fourier transform, the second one uses the idea of building autocorrelation portraits of speech signals. The proposed approaches are quite simple to implement, although they require cyclic operators, since they work with pairs of samples from the original signal. Approach of the developed method was carried out with the problem of recognizing phraseological radio exchange messages in Russian. The algorithm with preliminary feature extraction provides a gain of 1.7% in recognition accuracy. The use of convolutional neural networks also provides an increase in recognition efficiency. The gain for autocorrelation portraits processing is about 3-4%. Quantization is used to optimize the proposed models. The algorithm's performance increased by 2.8 times after the quantization. It was also possible to increase accuracy of recognition by 1-2% using digital signal processing algorithms. An important feature of the proposed algorithms is the possibility of generalizing them to arbitrary data with time correlation. The speech message preprocessing algorithms discussed in this article are based on classical digital signal processing algorithms. The idea of constructing autocorrelation portraits based on the time series of a signal has a novelty. At the same time, this approach ensures high recognition accuracy. However, the study also showed that all the algorithms under consideration perform quite poorly under the influence of strong noise. feature extraction; artificial neural networks; recurrent neural networks; convolutional neural networks; speech messages; autocorrelation function; phraseological radio exchange +
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speech messages may adversely affect the aircraft pilot. It is clear that the reception is carried out with aircraft and other noises. Therefore, it is necessary to develop high-quality speech message preprocessing algorithms for feature extraction in order to build automated systems for the phraseological messages recognition. Moreover, such a system should be invariant to the gender and personal characteristics of the voice. Finally, efficient preprocessing and processing in real-time is required. Obviously, the use of passenger transport carries certain risks. This also applies to aviation security. One of the factors that can be distinguished from human factors is pilot fatigue and workload. Thus, the article considers the applied task of reducing the aircraft pilot workload based on automated speech messages recognition. At the same time, radio exchange is the necessary for transmitting and receiving messages from air traffic control to ensure flight safety. Phraseological radio exchange allows dispatchers and pilots to control the situation on the ground and in the air. However, today radio traffic is not absolutely stable. It is often subject to various kinds of interference, which can greatly distort messages. Sometimes this problem deals with duplicating information, but the time spent can affect the level of flight safety. For example, in different conditions information about a flight height level change or an alternate landing airport will be extremely important. As for the noise-resistant transmission problem, it is possible to use adaptive filtering methods [13; 14; 15]. Application of simulation algorithms is necessary to analyze the quality of digital signal processing.