  History and future contextual information are known to be important for
accurate acoustic modeling. However, acquiring future context brings latency
for streaming ASR. In this paper, we propose a new framework - Chunking,
Simulating Future Context and Decoding (CUSIDE) for streaming speech
recognition. A new simulation module is introduced to recursively simulate the
future contextual frames, without waiting for future context. The simulation
module is jointly trained with the ASR model using a self-supervised loss; the
ASR model is optimized with the usual ASR loss, e.g., CTC-CRF as used in our
experiments. Experiments show that, compared to using real future frames as
right context, using simulated future context can drastically reduce latency
while maintaining recognition accuracy. With CUSIDE, we obtain new
state-of-the-art streaming ASR results on the AISHELL-1 dataset.
