Veuillez utiliser cette adresse pour citer ce document : https://di.univ-blida.dz/jspui/handle/123456789/13156
Titre: Multilingual voice recognition using deep learning for human-drone interaction
Auteur(s): Mebarkia, Nihad
Kacel, Yasmine; Choutri, Kheireddine (promoteur); Lagha, Mohand (promoteur)
Mots-clés: Speech recognition; DNN; UAV, ; Control; Human-Drone Interaction
Date de publication: 2021
Editeur: Université Blida 01
Résumé: 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.
Description: Mémoire de Master option Avionique .-Numéro de Thèse 050/2021
URI/URL: http://di.univ-blida.dz:8080/jspui/handle/123456789/13156
Collection(s) :Mémoires de Master

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