Comparative analysis of speech recognition algorithms in UAV voice control system
DOI:
https://doi.org/10.18372/2310-5461.38.12858Keywords:
MEL scale, BARK scale, UAV, speech recognition, MFCC, minimum mean square errorAbstract
The article proposes to perform a comparative analysis of the presented algorithms for processing voice control signals for an unmanned aerial vehicle, which can be implemented on processors with low computing power using online processing in real time. It is shown that these approaches are effective in improving the accuracy of speech recognition in the presence of various types of noise and a sound-reflecting control environment, which is an important problem in voice control systems for an unmanned aerial vehicle. An algorithm for calculating the mel-frequency cepstral coefficients, which appear in the role of the main features of speech recognition, is presented. A comparative analysis of two methods of distinguishing informative features of speech recognition in the voice control system of an unmanned aerial vehicle was made, namely, mel-frequency cepstral factors and the coefficients obtained with the aid of a linear prediction algorithm, where as a result of the conducted scientific experiment, under the influence of given noise, it was concluded that in these problems, the optimal method of exclusion is the mel-frequency cepstral factors, since they show the best value for obsalutnomu criterion of speech recognition quality. The expediency of using the proposed system for recognizing voice commands of an unmanned aerial vehicle based on the cepstral analysis is substantiated and experimentally proved. The obtained results of the experimental research allow to draw a conclusion about the advisability of further practical application of the developed system for recognizing voice commands for the control of an unmanned aerial vehicle on the basis of a cepstral analysis.
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