  Currently, unmanned aerial vehicles, such as drones, are becoming a part of
our lives and reaching out to many areas of society, including the
industrialized world. A common alternative to control the movements and actions
of the drone is through unwired tactile interfaces, for which different remote
control devices can be found. However, control through such devices is not a
natural, human-like communication interface, which sometimes is difficult to
master for some users. In this work, we present a domain-based speech
recognition architecture to effectively control an unmanned aerial vehicle such
as a drone. The drone control is performed using a more natural, human-like way
to communicate the instructions. Moreover, we implement 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. The
conducted experiments involve participants giving voice commands to the drone
in both languages in order to compare the effectiveness of each of them,
considering the mother tongue of the participants in the experiment.
Additionally, different levels of distortion have been applied to the voice
commands in order to test the proposed approach when facing noisy input
signals. The obtained results show that the unmanned aerial vehicle is capable
of interpreting user voice instructions achieving an improvement in
speech-to-action recognition for both languages when using 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, whereas
using our phoneme matching approach the results are improved achieving 93.33%
and 100.00% accuracy for English and Spanish languages.
