Simulation of Car Driving by Voice Commands based on a Deep-Learning Model
Mostafa Jebbar, Abderrahim Maizate, Rachida Ait Abdelouahid · Procedia Computer Science · 2022
Speech recognition is the field of research concerning the ability of machines to accept human voice as input and interpret it with the highest probability. This paper aims at simulating the driving of a robot by speech recognition of the dialect language "Darija" using the training of a deep-learning model, then the validation of the model will be done by simulation on the ROS environment, the simulation consists of driving a robot car that moves by voice commands in Darija. We develop here a deep-learning model based on TensorFlow to recognize this language. This model has been successfully tested in a car driving simulator (Robot Operating System, ROS) with the robot HUSKY with success rates up to 90% of recognition.