  There is often a trade-off between performance and latency in streaming
automatic speech recognition (ASR). Traditional methods such as look-ahead and
chunk-based methods, usually require information from future frames to advance
recognition accuracy, which incurs inevitable latency even if the computation
is fast enough. A causal model that computes without any future frames can
avoid this latency, but its performance is significantly worse than traditional
methods. In this paper, we propose corresponding revision strategies to improve
the causal model. Firstly, we introduce a real-time encoder states revision
strategy to modify previous states. Encoder forward computation starts once the
data is received and revises the previous encoder states after several frames,
which is no need to wait for any right context. Furthermore, a CTC spike
position alignment decoding algorithm is designed to reduce time costs brought
by the revision strategy. Experiments are all conducted on Librispeech
datasets. Fine-tuning on the CTC-based wav2vec2.0 model, our best method can
achieve 3.7/9.2 WERs on test-clean/other sets, which is also competitive with
the chunk-based methods and the knowledge distillation methods.
