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.