Introducing Temporal Asymmetries in Feature Extraction for Automatic Speech Recognition

We propose a new auditory inspired feature extraction technique for automatic speech recognition (ASR). Features are extracted by filtering the temporal trajectory of spectral energies in each critical band of speech by a bank of finite impulse response (FIR) filters. Impulse responses of these filters are derived from a modified Gabor envelope in order to emulate asymmetries of the temporal receptive field (TRF) profiles observed in higher level auditory neurons. We obtain $11.4\%$ relative improvement in word error rate on OGI-Digits database and, $3.2\%$ relative improvement in phoneme error rate on TIMIT database over the MRASTA technique.