Recognition of radio exchange voice messages in aviation based on correlation analysis

The paper considers the problem of speech messages recognition in phraseological radio exchange for tasks of civil aviation. The introduction substantiates the relevance of this problem. The following are research methods based on correlation analysis. Finally, a description of the experiment and the results of the recognition algorithms based on correlation analysis are given. Various variants were recorded for five speech messages and spectral representations of such signals were constructed. Spectral transform can be obtained either using specialized software or based on the Fourier transform of the signal in the time domain. To obtain a more universal reference signal and eliminate the influence of interference, the spectral components of the same speech message recorded several times were averaged. In fact, three spectra of the same speech message were used for averaging. This spectrum averaging over three training components provided a reference sample of phrases or patterns for each phrase, and reduced the influence of additive white Gaussian noise in the reference. Later, on the basis of correlation analysis, the connections between test phrases and all patterns were calculated. On the basis of these connections, a correlation matrix of reference phrases is built. Research has shown that phrases spoken by one person were highly correlated. The analysis showed that the choice of the class (the content of the speech message) when solving the recognition problem corresponding to the value of the correlation coefficient closest to one provides over 90% of correct recognitions on a test sample containing a total of 100 phrases, 20 for each phrase. It should be noted that, when recording test messages, an additive white Gaussian noise was additionally present as a background, reproduced by another audio device. In the case of information analysis without artificially generated noise, the probability of correct recognition for a test sample of 100 phrases, 20 for each phrase, is 100% when using correlation analysis.