Large Vocabulary Speech Recognition Using Deep Tensor Neural Networks

Recently, we proposed and developed the context-dependent deep neural network hidden Markov models (CD-DNN-HMMs) for large vocabulary speech recognition and achieved highly promising recognition results including over one third fewer word errors than the discriminatively trained, conventional HMM-based systems on the 300hr Switchboard benchmark task. In this paper, we extend DNNs to deep tensor neural networks (DTNNs) in which one or more layers are double-projection and tensor layers. The basic idea of the DTNN comes from our realization that many factors interact with each other to predict the output. To represent these interactions, we project the input to two nonlinear subspaces through the double-projection layer and model the interactions between these two subspaces and the output neurons through a tensor with three-way connections. Evaluation on 30hr Switchboard task indicates that DTNNs can outperform DNNs with similar number of parameters with 5% relative word error reduction.