Auditory feature representation using convolutional restricted Boltzmann machine and Teager energy operator for speech recognition

Abstract

In this letter, authors propose an auditory feature representation technique with the filterbank learned using an annealing dropout convolutional restricted Boltzmann machine (ConvRBM) and noise-robust energy estimation using the Teager energy operator (TEO). TEO is applied on each subband of ConvRBM filterbank and pooled later to get the short-term spectral features. Experiments on AURORA 4 database show that the proposed features perform better than the Mel filterbank features. The relative improvement of 2.59%–11.63% and 1.26%–6.87% in word error rate is achieved using the time delay neural network and the bidirectional long short-term memory models, respectively.