DEEP RECURRENT REGULARIZATION NEURAL NETWORK FOR SPEECH RECOGNITION

Abstract

This paper presents a deep recurrent regularization neural network (DRRNN) for speech recognition. Our idea is to build a regularization neural network acoustic model by conducting the hybrid Tikhonov and weight-decay regularization which compensates the variations due to the input speech as well as the model parameters in the restricted Boltzmann machine as a pre-training stage for feature learning and structural modeling. In addition, a new backpropagation through time (BPTT) algorithm is developed by extending the truncated minibatch training for recurrent neural network where the minibatch BPTT is not only performed in recurrent layer but also in feedforward layer. The DRRNN acoustic model is accordingly established to capture the temporal correlation in a regularization neural network. Experimental results on the tasks of RM and Aurora4 show the effectiveness and robustness of using DRRNN for speech recognition.