  In recent years, Transformer networks have shown remarkable performance in
speech recognition tasks. However, their deployment poses challenges due to
high computational and storage resource requirements. To address this issue, a
lightweight model called EfficientASR is proposed in this paper, aiming to
enhance the versatility of Transformer models. EfficientASR employs two primary
modules: Shared Residual Multi-Head Attention (SRMHA) and Chunk-Level
Feedforward Networks (CFFN). The SRMHA module effectively reduces redundant
computations in the network, while the CFFN module captures spatial knowledge
and reduces the number of parameters. The effectiveness of the EfficientASR
model is validated on two public datasets, namely Aishell-1 and HKUST.
Experimental results demonstrate a 36% reduction in parameters compared to the
baseline Transformer network, along with improvements of 0.3% and 0.2% in
Character Error Rate (CER) on the Aishell-1 and HKUST datasets, respectively.
