Minimum classification error rate pattern recognition approach for speech and language processing

Abstract

Summary form only given. Minimum classification error (MCE) rate pattern recognition approach is a fast moving research area and broadly applied to pattern recognition problems in speech and language processing. We give an overview of the basic MCE classifier design algorithms as well as the more advanced extensions of the MCE approach. We differentiate the classifier design by way of distribution estimation and by way of the discriminant function methods according to the minimum classification error rate paradigm. We study the practical issues in system implementation and highlight the application perspectives of applying MCE classifier design to practical speech and language processing systems.