  The use of future contextual information is typically shown to be helpful for
acoustic modeling. However, for the recurrent neural network (RNN), it's not so
easy to model the future temporal context effectively, meanwhile keep lower
model latency. In this paper, we attempt to design a RNN acoustic model that
being capable of utilizing the future context effectively and directly, with
the model latency and computation cost as low as possible. The proposed model
is based on the minimal gated recurrent unit (mGRU) with an input projection
layer inserted in it. Two context modules, temporal encoding and temporal
convolution, are specifically designed for this architecture to model the
future context. Experimental results on the Switchboard task and an internal
Mandarin ASR task show that, the proposed model performs much better than long
short-term memory (LSTM) and mGRU models, whereas enables online decoding with
a maximum latency of 170 ms. This model even outperforms a very strong
baseline, TDNN-LSTM, with smaller model latency and almost half less
parameters.
