COST278 and ISCA Tutorial and Research Workshop (ITRW) on Robustness Issues in Conversational Interaction
University of East Anglia, Norwich, UK
Kernel Discriminant Analysis (KDA) has been successfully applied to many pattern recognition problems. KDA transforms the original problem into a space of dimension N where N is the number of training vectors. For speech recognition, N is usually prohibitively high increasing computational requirements beyond current computational capabilities. In this paper, we provide a formulation of a subspace version of KDA that enables its application to speech recognition, thus conveniently enabling nonlinear feature space transformations that result in discriminatory lower dimensional features.
Bibliographic reference. Erdogan, Hakan (2004): "Subspace kernel discriminant analysis for speech recognition", In Robust2004, paper 26.