Hidden Markov model/Gaussian mixture models (HMM/GMM) based voice command system: A way to improve the control of remotely operated robot arm TR45

摘要

A speech control system for a didactic manipulator arm TR45 is designed as an agent in a tele-manipulator system command. Robust Hidden Markov Model (HMM) and Gaussian Mixture models (GMM) are applied in spotted words recognition system with Cepstral coefficients with energy and differentials as features. The HMM and GMM are used independently in automatic speech recognition agent to detect spotted words and recognize them. A decision block will generate the appropriate command and send it to a parallel port of the Personal Computer (PC). To implement the approach on a real-time application, a PC parallel port interface was designed to control the movement of robot motors using a wireless communication component. The user can control the movements of robot arm using a normal speech containing spotted words.   Key words: Human-machine interaction, hidden Markov model, Gaussian mixture models, artificial intelligence, automatic guided vehicle, voice command, robot arm and robotics.