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The use of future contextual information is typically shown to be helpful foracoustic modeling. Recently, we proposed a RNN model called minimal gatedrecurrent unit with input projection (mGRUIP), in which a context module namelytemporal convolution, is specifically designed to model the future context.This model, mGRUIP with context module (mGRUIP-Ctx), has been shown to be ableof utilizing the future context effectively, meanwhile with quite low modellatency and computation cost. In this paper, we continue to improve mGRUIP-Ctxwith two revisions: applying BN methods and enlarging model context.Experimental results on two Mandarin ASR tasks (8400 hours and 60K hours) showthat, the revised mGRUIP-Ctx outperform LSTM with a large margin (11% to 38%).It even performs slightly better than a superior BLSTM on the 8400h task, with33M less parameters and just 290ms model latency.

Improving Gated Recurrent Unit Based Acoustic Modeling with Batch Normalization and Enlarged Context