Stochastic Recurrent Neural Network for Speech Recognition

This paper presents a new stochastic learning approach to construct a latent variable model for recurrent neural network (RNN) based speech recognition. A hybrid generative and discriminative stochastic network is implemented to build a deep classification model. In the implementation, we conduct stochastic modeling for hidden states of recurrent neural network based on the variational auto-encoder. The randomness of hidden neurons is represented by the Gaussian distribution with mean and variance parameters driven by neural weights and learned from variational inference. Importantly, the class labels of input speech frames are incorporated to regularize this deep model to sample the informative and discriminative features for reconstruction of classification outputs. We accordingly propose the stochastic RNN (SRNN) to reflect the probabilistic property in RNN classification system. A stochastic error backpropagation algorithm is implemented. The experiments on speech recognition using TIMIT and Aurora4 show the merit of the proposed SRNN.