Paul A. Karger
SOUPS 2006
A pattern recognizer is usually a modular system which consists of a feature extractor module and a classifier module. Traditionally, these two modules have been designed separately, which may not result in an optimal recognition accuracy. To alleviate this fundamental problem, the authors have developed a design method, named Discriminative Feature Extraction (DFE), that enables one to design the overall recognizer, i.e., both the feature extractor and the classifier, in a manner consistent with the objective of minimizing recognition errors. This paper investigates the application of this method to designing a speech recognizer that consists of a filter-bank feature extractor and a multi-prototype distance classifier. Carefully investigated experiments demonstrate that DFE achieves the design of a better recognizer and provides an innovative recognition-oriented analysis of the filter-bank, as an alternative to conventional analysis based on psychoacoustic expertise or heuristics. ©2001 IEEE.
Paul A. Karger
SOUPS 2006
Qian Huang, George C. Stockman
ICPR 1994
Takashi Saito
IEICE Transactions on Information and Systems
Daniel A. Vaquero, Rogerio S. Feris, et al.
WACV 2009