Afficher la notice abrégée
dc.contributor.author |
Mebarkia, Nihad |
|
dc.contributor.author |
Kacel, Yasmine;
Choutri, Kheireddine (promoteur);
Lagha, Mohand (promoteur) |
|
dc.date.accessioned |
2021-11-25T09:09:32Z |
|
dc.date.available |
2021-11-25T09:09:32Z |
|
dc.date.issued |
2021 |
|
dc.identifier.uri |
http://di.univ-blida.dz:8080/jspui/handle/123456789/13156 |
|
dc.description |
Mémoire de Master option Avionique .-Numéro de Thèse 050/2021 |
fr_FR |
dc.description.abstract |
Over the past several years, human-drone interaction have got an important part from the scientific researchs. When interacting with a drone, humans take on many responsibilities. Their role is determined by the drone’s application and the amount of
autonomy. The purpose of this work is to control the movement of the unmanned aerial vehicle (UAV) using a multilingual speech recognition system. For that, a deep neural network (DNN) is trained to recognize the user’s speech from many languages and then
generate the desired control command. We conducted experiments involving participants giving voice commands in order to compare the effectivencess of each database. Hardware implementation of the designed system proof its high accuracy recognition and control
simplicity. |
fr_FR |
dc.language.iso |
en |
fr_FR |
dc.publisher |
Université Blida 01 |
fr_FR |
dc.subject |
Speech recognition;
DNN;
UAV, ;
Control;
Human-Drone Interaction |
fr_FR |
dc.title |
Multilingual voice recognition using deep learning for human-drone interaction |
fr_FR |
dc.type |
Thesis |
fr_FR |
Fichier(s) constituant ce document
Ce document figure dans la(les) collection(s) suivante(s)
Afficher la notice abrégée