  Currently, the chunk-wise schemes are often used to make Automatic Speech
Recognition (ASR) models to support streaming deployment. However, existing
approaches are unable to capture the global context, lack support for parallel
training, or exhibit quadratic complexity for the computation of multi-head
self-attention (MHSA). On the other side, the causal convolution, no future
context used, has become the de facto module in streaming Conformer. In this
paper, we propose SSCFormer to push the limit of chunk-wise Conformer for
streaming ASR using the following two techniques: 1) A novel cross-chunks
context generation method, named Sequential Sampling Chunk (SSC) scheme, to
re-partition chunks from regular partitioned chunks to facilitate efficient
long-term contextual interaction within local chunks. 2)The Chunked Causal
Convolution (C2Conv) is designed to concurrently capture the left context and
chunk-wise future context. Evaluations on AISHELL-1 show that an End-to-End
(E2E) CER 5.33% can achieve, which even outperforms a strong time-restricted
baseline U2. Moreover, the chunk-wise MHSA computation in our model enables it
to train with a large batch size and perform inference with linear complexity.
