Acoustic modeling using auditory model features and Convolutional neural Network

Abstract

The state of art automatic speech recognition systems use Deep Neural Networks(DNN) for acoustic modeling. More recently, Convolutional neural Networks(CNN) have shown substantial acoustic modelling capabilities due to its ability to deal with structural locality in the feature space. In this paper, a detailed study of CNN based acoustic models on TIMIT database has been performed. For feature extraction an biologically motivated auditory model is simulated using Patterson and Holdsworth filter bank. MFSC features are also extracted for comparison. The experiments show that CNN with the auditory model features outperforms the conventional acoustic models which use mel spectral features.