A hybrid CNN-LiGRU acoustic modeling using raw waveform sincnet for Hindi ASR

Abstract

Deep Neural Network (DNN) is currently playing the most vital role in Automatic Speech Recognition (ASR). Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are the advanced versions of DNN. CNN and RNN are right to deal with spatial and temporal properties of the speech signal, respectively, and both properties have a higher impact on accuracy. In today’s scenario, many acoustic modeling techniques often switches due to the battle of CNNs and RNNs. In the last few years, CNN, with raw speech signal, shows their superiority over precomputed acoustic features. Recently, a novel first convolution layer named SincNet was proposed to produce the interpretable filters with better accuracy. In this work, we proposed a hybrid SincNet-CNN-RNN architecture with low computation cost and high accuracy. Different configurations of the hybrid model were extensively examined to achieve this goal. All experiments were performed on the Hindi speech dataset.