This paper presents an in-depth study on a Sequentially Sampled Chunk
Conformer, SSC-Conformer, for streaming End-to-End (E2E) ASR. The SSC-Conformer
first demonstrates the significant performance gains from using the
sequentially sampled chunk-wise multi-head self-attention (SSC-MHSA) in the
Conformer encoder by allowing efficient cross-chunk interactions while keeping
linear complexities. Furthermore, it explores taking advantage of chunked
convolution to make use of the chunk-wise future context and integrates with
casual convolution in the convolution layers to further reduce CER. We verify
the proposed SSC-Conformer on the AISHELL-1 benchmark and experimental results
show that a state-of-the-art performance for streaming E2E ASR is achieved with
CER 5.33% without LM rescoring. And, owing to its linear complexity, the
SSC-Conformer can train with large batch sizes and infer more efficiently.

本文研究了一种 Sequentially Sampled Chunk Conformer (SSC-Conformer) 模型，该模型通过使用 SSC-MHSA、分块卷积以及线性复杂度等技术，实现了端到端自动语音识别的最新水平，达到了 5.33% 的 CER（无需 LM rescoring），同时可用于高效训练和推理。