Speech Control Scheme Design and Simulation for UAV Based on HMM and RNN

doi:10.16182/j.issn1004731x.joss.18-0167

Abstract

Abstract: In order to simplify the operation and avoid the misoperation of UAVs, Based on Hidden Markov Model and Recurrent Neural Networks, a speech control scheme for UAVs is designed. In this scheme, HMM is used to train and recognize the speech command samples of UAVs. HMM is used to pick out the error commands, RNN is used to train the sets of UAVs commands, and the next command based on the training result is predicted. It is determined whether to execute or not by calculating the correlation between commands recognized by HMM and predicted by RNN. The simulation results show the recognition rate of wrong command is as high as 61.90%, and the overall error rate is down to 1.43%. All show the excellent performance of this scheme.

Key words: UAVs(Unmanned Aerial Vehicles), speech control, HMM(Hidden Markov Model), RNN(Recurrent Neural Networks)

CLC Number:

TN912.34

Zhou Nan, Ai Jianliang. Speech Control Scheme Design and Simulation for UAV Based on HMM and RNN[J]. Journal of System Simulation, 2020, 32(3): 464-471.

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