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Implementation of Speech Recognition and Flight Controller Based on Deep Learning for Control to Primary Control Surface of Aircraft

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(9), pp.57-64
  • DOI : 10.9708/jksci.2021.26.09.057
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 25, 2021
  • Accepted : September 24, 2021
  • Published : September 30, 2021

Hwa-La Hur 1 Tae-Sun Kim 1 Myeong-Chul Park 1

1경운대학교

Accredited

ABSTRACT

In this paper, we propose a device that can control the primary control surface of an aircraft by recognizing speech commands. The speech command consists of 19 commands, and a learning model is constructed based on a total of 2,500 datasets. The training model is composed of a CNN model using the Sequential library of the TensorFlow-based Keras model, and the speech file used for training uses the MFCC algorithm to extract features. The learning model consists of two convolution layers for feature recognition and Fully Connected Layer for classification consists of two dense layers. The accuracy of the validation dataset was 98.4%, and the performance evaluation of the test dataset showed an accuracy of 97.6%. In addition, it was confirmed that the operation was performed normally by designing and implementing a Raspberry Pi-based control device. In the future, it can be used as a virtual training environment in the field of voice recognition automatic flight and aviation maintenance.

Citation status

* References for papers published after 2022 are currently being built.