Voice Command Recognition for Drone Control by Deep Neural Networks on Embedded System

Cengizhan Yapicioglu, Zumray Dokur, Tamer Olmez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Speech recognition and its applications for controlling a system has been an important and attractive issue over the last few decades. Controlling electronic devices by speech commands allows us to manage systems quickly and easily since users would not need any additional information or remote controller. Being able to communicate a system by using speech commands also brings with the requirements of fast and accurate response. So, at the present, speech recognition algorithms are extensively performing on high performance computers. However, the improvements of system on a chip (SoC) board and deep neural network based algorithms, make it possible to execute such kind of programs on them. The proposed study defines a model for controlling a drone system by using Turkish speech directional commands in real time which is based on deep learning approaches using spectrogram images. At first, speech commands are detected in real time with the help of signal energy and zero crossing rate and these are converted to log spectrogram images. A CNN (three convolutional layers and a fully connected layer) structure is created and trained by feeding those images. Then, the trained model is moved to embedded board to achieve real time, low-cost performance. Speech commands are provided by the user instantaneously and transferred to the model as an input for decision. Then, algorithm decides which directional command is given by the user and desired operation is performed on the drone system. It is observed that, by using the proposed model, accuracies of 95.72% for offline dataset and 92,88% for real time classification are obtained.

Original languageEnglish
Title of host publication2021 8th International Conference on Electrical and Electronics Engineering, ICEEE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-72
Number of pages8
ISBN (Electronic)9780738113579
DOIs
Publication statusPublished - 9 Apr 2021
Event8th International Conference on Electrical and Electronics Engineering, ICEEE 2021 - Antalya, Turkey
Duration: 9 Apr 202111 Apr 2021

Publication series

Name2021 8th International Conference on Electrical and Electronics Engineering, ICEEE 2021

Conference

Conference8th International Conference on Electrical and Electronics Engineering, ICEEE 2021
Country/TerritoryTurkey
CityAntalya
Period9/04/2111/04/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • convolutional neural networks
  • deep learning
  • embedded systems
  • image processing
  • spectrogram
  • speech processing
  • speech recognition

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