Filter bank design based on discriminative feature extraction

A filter bank model, which achieves minimum error, is investigated in this paper. A bank-of-filter feature extractor module is comprehensively optimized with the classifier´s parameters for minimization of the errors occurring at the back-end classifier. The method has been applied to readjusting Mel-scale and Bark-scale based filter banks for the Japanese vowel recognition task, the framework being provided by the minimum classification error (MCE)/generalised probabilistic descent method (GPD). The results show suggestive phenomena underlying the accuracy of the proposed approach