Optimizing features and models using the minimum classification error criterion

Discriminative feature extraction (DFE) has been proposed as a extension of MCE/GPD for the joint optimization of features and models. This study presents various configurations of this discriminative framework aimed at optimizing filter-bank parameters, using cepstrum and delta cepstrum as features, within an HMM-based system. Features and models are optimized either jointly or separately. Experimental results on the ISOLET database show that the joint optimization of features and models realizes the best performance: more than 13% absolute error rate reduction on the E-set task compared to an MLE-trained system using MFCCs and more than 1.85% absolute error rate reduction compared to an MCE-trained system using MFCCs.