Hagen Soltau, Lidia Mangu, et al.
ASRU 2011
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.
Hagen Soltau, Lidia Mangu, et al.
ASRU 2011
C. Neti, Salim Roukos
ASRU 1997
R.A. Gopinath, Markus Lang, et al.
ICIP 1994
Tetsuro Morimura, Sei Kato
ICPR 2012