Kernel ridge regression method applied to speech recognition problem: A novel approach

Abstract

Speech recognition is the important problem in pattern recognition research field. In this paper, the kernel ridge regression method is proposed to be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the kernel ridge regression method outperforms the current state of the art Hidden Markov Model method in speech recognition problem in terms of sensitivity performance measure and calculation speed of training process.