Title :
Feature Transformation and Model Design Using Minimum Classification Error
Author :
Ratnagiri, M.V. ; Rabiner, L. ; Biing-Hwang Juang
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of Rutgers, NJ, USA
Abstract :
A Minimum Classification Error (MCE) based recognition system that also estimates a global feature transformation matrix has been implemented. Unlike earlier studies, we make the explicit assumption that the covariance matrix of the Gaussian mixtures is diagonal when estimating the transformation matrix. This is necessary for mathematical consistency between the model and the transformation matrix estimates. Experimental results show a reduction of up to 50% in the word error rate as compared to Maximum Likelihood estimation.
Keywords :
Gaussian processes; covariance matrices; maximum likelihood estimation; speech recognition; Gaussian mixtures; covariance matrix; global feature transformation matrix; maximum likelihood estimation; minimum classification error; speech recognition system; Computational modeling; Covariance matrix; Feature extraction; Hidden Markov models; Maximum likelihood estimation; Noise; feature transformation; speech processing;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.122
