Automatic speech recognition based on conformer with convolution enhanced channel attention block

Conformer has demonstrated outstanding performance in the field of speech recognition, representing a highly effective improvement over the Transformer model. Subsequently, research on the integration of encoder blocks emerged, such as Blockformer. In this paper, we investigated the integration method of Conformer's encoder blocks and proposed a more efficient approach for leveraging information from multiple blocks, which we refer to as Convolution-Enhanced Channel Attention Block Integration. We achieved a Character Error Rate (CER) of 4.58% on the Aishell-1 dataset.