Faculté des Sciences appliquées
Faculté des Sciences appliquées

Master thesis : Drone control through a vocal interface

Pirlet, Matthias ULiège
Promotor(s) : Louppe, Gilles ULiège
Date of defense : 5-Sep-2022/6-Sep-2022 • Permalink :
Title : Master thesis : Drone control through a vocal interface
Translated title : [fr] Contrôle de drone à travers une interface vocale
Author : Pirlet, Matthias ULiège
Date of defense  : 5-Sep-2022/6-Sep-2022
Advisor(s) : Louppe, Gilles ULiège
Committee's member(s) : Debruyne, Christophe ULiège
Wehenkel, Louis ULiège
Greffe, Christophe 
Language : English
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master : ingénieur civil en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées


[en] Today the use of drones is widely spread for many tasks, but for some of these,
such as firefighting, it is vital that the operator’s hands are kept free to do their job
properly. Hopefully speech recognition is an exploding discipline in deep learning. This
work therefore focuses on finding a deep learning model that recognises spoken com-
mands to control a drone from a pre-defined set.
The first part of the work was to build a training dataset of commands with the
combination of complete commands generated through the use of text-to-speech APIs
and hand-crafted commands. These were created thanks to the concatenation of an
open source spoken words dataset and another set of spoken words acquired through a
web platform created in order to complete the missing words of the vocabulary com-
mand set. The testing set was acquired by asking people to record complete commands
under real conditions.
The second part of the work focuses more on the different models that could be
developed and all the techniques that can be used. These are presented as an ablation
study, in order to improve the results on a test set in real conditions. Several meth-
ods were applied in order to achieve the final goal: the first is the use of computer
vision models where the input of these models is a simple spectrogram of the different
commands. The results using these types of models were not as good as those of the
new models which take directly the raw waveform as input and combines vision, atten-
tion and self-supervised learning. The best version of this model obtains a F1-Score of
0.9973 on a real conditions dataset.



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  • Pirlet, Matthias ULiège Université de Liège > Master ingé. civ. sc. don. à . fin.


Committee's member(s)

  • Debruyne, Christophe ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Représentation et ingénierie des données
    ORBi View his publications on ORBi
  • Wehenkel, Louis ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
  • Greffe, Christophe Generix
  • Total number of views 124
  • Total number of downloads 209

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