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Training deep recurrent neural network (RNN) architectures is complicated dueto the increased network complexity. This disrupts the learning of higher orderabstracts using deep RNN. In case of feed-forward networks training deepstructures is simple and faster while learning long-term temporal informationis not possible. In this paper we propose a residual memory neural network(RMN) architecture to model short-time dependencies using deep feed-forwardlayers having residual and time delayed connections. The residual connectionpaves way to construct deeper networks by enabling unhindered flow of gradientsand the time delay units capture temporal information with shared weights. Thenumber of layers in RMN signifies both the hierarchical processing depth andtemporal depth. The computational complexity in training RMN is significantlyless when compared to deep recurrent networks. RMN is further extended asbi-directional RMN (BRMN) to capture both past and future information.Experimental analysis is done on AMI corpus to substantiate the capability ofRMN in learning long-term information and hierarchical information. Recognitionperformance of RMN trained with 300 hours of Switchboard corpus is comparedwith various state-of-the-art LVCSR systems. The results indicate that RMN andBRMN gains 6 % and 3.8 % relative improvement over LSTM and BLSTM networks.

Residual Memory Networks: Feed-forward approach to learn long temporal dependencies