Voice control for a gripper using Mel-Frequency Cepstral Coefficients and Gaussian Mixture Models

This work presents an implementation of a speaker-dependent speech recognition system used to control a gripper. The application was made using MATLAB and the gripper was assembled using the Lego Mindstorm NXT robotic kit. Four commands are implemented for controlling the gripper: Open, close, rotate left and rotate right. The development was divided into two stages. In training stage, we use Mel Frequency Cepstral Coefficients (MFCCs) and Gaussian Mixture Models (GMMs) to generate a representation of each defined command. Then, in testing stage, those models are used to identify the speaker's utterance and send the command to the actuator. Finally, we present test results that show a performance of 95.09% for our system, and then we compare it with similar works.