Discriminative feature extraction application to filter bank design

This paper investigates the design of a filter bank model by the discriminative feature extraction method (DFE). A filter bank-based feature extractor is optimized with the classifier´s parameters for the minimization of the errors occurring at the back-end classification process. The framework of minimum classification error/generalized probabilistic descent method (MCE/GPD) is used as the basis for optimization. The method is first tested in a vowel recognition task. Analysis of the process shows how DFE extracts those parts of the spectrum that are relevant to discrimination. Then the method is applied to a multi-speaker word recognition system intended to act as telephone directory assistance operator