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Unmanned aerial vehicle control through domain-based automatic speech recognition

Contreras, Ruben, Ayala, Angel and Cruz, Francisco 2020, Unmanned aerial vehicle control through domain-based automatic speech recognition, Computers, vol. 9, no. 3, pp. 1-15, doi: 10.3390/computers9030075.

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Title Unmanned aerial vehicle control through domain-based automatic speech recognition
Author(s) Contreras, Ruben
Ayala, Angel
Cruz, FranciscoORCID iD for Cruz, Francisco orcid.org/0000-0002-1131-3382
Journal name Computers
Volume number 9
Issue number 3
Article ID 75
Start page 1
End page 15
Total pages 15
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2020-09
ISSN 2073-431X
Keyword(s) drone control
automatic speech recognition
robot simulator
Summary Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and extend to many areas of society, including the industrialized world. A common alternative for controlling the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices are used. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this research, we experimented with a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control was performed in a more natural, human-like way to communicate the instructions. Moreover, we implemented an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. We conducted experiments involving participants giving voice commands to the drone in both languages in order to compare the effectiveness of each, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion were applied to the voice commands to test the proposed approach when it encountered noisy input signals. The results obtained showed that the unmanned aerial vehicle was capable of interpreting user voice instructions. Speech-to-action recognition improved for both languages with phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions, respectively. However, with our phoneme matching approach the results are improved, yielding 93.33% accuracy for English and 100.00% accuracy for Spanish.
Language eng
DOI 10.3390/computers9030075
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30143253

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.