Algorithms, Vol. 16, Pages 90: The Use of Correlation Features in the Problem of Speech Recognition

Algorithms, Vol. 16, Pages 90: The Use of Correlation Features in the Problem of Speech Recognition

Algorithms doi: 10.3390/a16020090

Authors: Andriyanov

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. Approbation 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.