Feature extraction using non-linear transformation for robust speech recognition on the AURORA database

We evaluate the performance of several feature sets on the AURORA task as defined by ETSI. We show that after a non-linear transformation, a number of features can be effectively used in a HMM-based recognition system. The non-linear transformation is computed using a neural network which is discriminatively trained on the phonetically labeled (forcibly aligned) training data. A combination of the non-linearly transformed PLP, MSG and TRAP features yields a 63% improvement in error rate as compared to a baseline MFCC features. The use of the non-linearly transformed RASTA-like features, with system parameters scaled down to take into account the ETSI imposed memory and latency constraints, still yields a 40% improvement in error rate.