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In this paper, we propose a novel neural network structure, namely\emph{feedforward sequential memory networks (FSMN)}, to model long-termdependency in time series without using recurrent feedback. The proposed FSMNis a standard fully-connected feedforward neural network equipped with somelearnable memory blocks in its hidden layers. The memory blocks use atapped-delay line structure to encode the long context information into afixed-size representation as short-term memory mechanism. We have evaluated theproposed FSMNs in several standard benchmark tasks, including speechrecognition and language modelling. Experimental results have shown FSMNssignificantly outperform the conventional recurrent neural networks (RNN),including LSTMs, in modeling sequential signals like speech or language.Moreover, FSMNs can be learned much more reliably and faster than RNNs or LSTMsdue to the inherent non-recurrent model structure.

Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency