Pratibha Jain

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 (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.

Multistream network feature processing for a distributed speech recognition system

A distributed voice recognition system and method for obtaining acoustic features and speech activity at multiple frequencies by extracting high frequency components thereof on a device, such as a subscriber station and transmitting them to a network server having multiple stream processing capability, including cepstral feature processing, MLP nonlinear transformation processing, and multiband temporal pattern architecture processing. The features received at the network server are processed using all three streams, wherein each of the three streams provide benefits not available in the other two, thereby enhancing feature interpretation. Feature extraction and feature interpretation may operate at multiple frequencies, including but not limited to 8 kHz, 11 kHz, and 16 kHz.