Voice command system based on pipelining classifiers GMM-HMM

Abstract

Details of designing and developing a voice guiding system for a robot arm is presented. The features combination technique is investigated and then a hybrid method for classification is applied. Based on research and experimental results, more features will increase the rate of recognition in automatic speech recognition. Thus combining classical components used in ASR system such as Crossing Zero, energy, Mel frequency cepstral coefficients with wavelet transform (to extract meaningful formants parameters) followed by a pipelining ordered classifiers GMM and HMM has contributed in reducing the error rate considerably. To implement the approach on a real-time application, a PC interface was designed to control the movements of a four degree of freedom robot arm by transmitting the orders via RF circuits. The voice command system for the robot is designed and tests showed an Improvement by combining techniques.