2000 Articles

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

Sharma, Sangita; Ellis, Daniel P. W.; Kajarekar, Sachin; Jain, Pratibha; Hermansky, Hynek

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 (perceptive linear predictive coefficients), MSG (modulation filtered spectrogram) and TRAP (temporal pattern) features yields a 63% improvement in error rate as compared to baseline me frequency cepstral coefficients 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.