Controlling a robot still requires traditional user interfaces. A more intuitive approach is using verbal or gesture commands . In this paper, we propose a robotic arm that can recognize the human voice commands. The Speech recognition is an essential asset for the robot, enhancing its ability to interact with human beings using their natural form of communication. The approach is verified by deploying the robotic arm into different environments with low, high and medium noise perturbations, where it is tested to perform a set of tasks. With this approach, we have successfully reduced the response time, enhanced the accuracy of the robotic arm to grasp the voice commands and reduced the sentence overlapping by a significant amount. The entire system is divided into three modules: the manipulator, the voice recognition moduleand the microcontroller. The robotic armis programmed to orient to the direction where the signal to noise ratio is maximum .
Speech recognition is a prominent technology, which helps us to develop a Natural language interface through speech for the Human-Robot Interaction (HRI). It allows the computer to take the spoken instructions, interpret it, and generate text from it . In this paper, we propose a phoneme based speech recognition system to control industrial robots. Speech recognition has become one of the popular interfaces when it comes to reducing robot operator's efforts to control and command the robot. This paper intends to investigate the potential of Linear Predictive coding technique to develop a stable and robust phoneme speech recognition system for robotics applications. Our system is divided into three segments: a microphone array, a voice module, and a 3-DOF robotic arm. To validate our approach, we have performed tests with simple and complex sentences for various robotics activities like manipulating a cube and pick and place tasks. Moreover, we also analyzed the test result to rectify the problems and limitations in our approach. The paper presents all the test results which we have achieved through conducting experiments on our project .
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Controlling a robot still requires traditional user interfaces. A more intuitive approach is using verbal or gesture commands
Speech recognition is a prominent technology, which helps us to develop a Natural language interface through speech for the Human-Robot Interaction (HRI). It allows the computer to take the spoken instructions, interpret it, and generate text from it
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robotic arm that can recognize the human voice commands. The Speech recognition is an essential asset for the robot, enhancing its ability to interact with human beings using their natural form of communication. The approach is verified by deploying the robotic arm into different environments with low, high and medium noise perturbations, where it is tested to perform a set of tasks. With this approach, we have successfully reduced the response time, enhanced the accuracy of the robotic arm to grasp the voice commands and reduced the sentence overlapping by a significant amount. The entire
phoneme based speech recognition system to control industrial robots. Speech recognition has become one of the popular interfaces when it comes to reducing robot operator's efforts to control and command the robot. This paper intends to investigate the potential of Linear Predictive coding technique to develop a stable and robust phoneme speech recognition system for robotics applications. Our
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modules: the manipulator, the voice recognition moduleand the microcontroller. The robotic armis programmed to orient to the direction where the signal to noise ratio is maximum
segments: a microphone array, a voice module, and a 3-DOF robotic arm. To validate our approach, we have performed tests with simple and complex sentences for various robotics activities like manipulating a cube and pick and place tasks. Moreover, we also analyzed the test result to rectify the problems and limitations in our approach. The paper presents all the test results which we have achieved through conducting experiments on our project