  The use of future contextual information is typically shown to be helpful for
acoustic modeling. Recently, we proposed a RNN model called minimal gated
recurrent unit with input projection (mGRUIP), in which a context module namely
temporal convolution, is specifically designed to model the future context.
This model, mGRUIP with context module (mGRUIP-Ctx), has been shown to be able
of utilizing the future context effectively, meanwhile with quite low model
latency and computation cost. In this paper, we continue to improve mGRUIP-Ctx
with two revisions: applying BN methods and enlarging model context.
Experimental results on two Mandarin ASR tasks (8400 hours and 60K hours) show
that, 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, with
33M less parameters and just 290ms model latency.
