Citrinet is an end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model. To capture local and global contextual information, 1D time-channel separable convolutions combined with sub-word encoding and squeeze-and-excitation (SE) are used in Citrinet, making the whole architecture to be as deep as including 23 blocks with 235 convolution layers and 46 linear layers. This pure convolutional and deep architecture makes Critrinet relatively slow at convergence. In this paper, we propose to introduce multi-head attentions together with feed-forward networks in the convolution module in Citrinet blocks while keeping the SE module and residual module unchanged. For speeding up, we remove 8 convolution layers in each attention-enhanced Citrinet block and reduce 23 blocks to 13. Experiments on the Japanese CSJ-500h and Magic-1600h dataset show that the attention-enhanced Citrinet with less layers and blocks and converges faster with lower character error rates than (1) Citrinet with 80\% training time and (2) Conformer with 40\% training time and 29.8\% model size.

本文介绍了一种基于卷积神经网络的语音识别模型Citrinet，利用多头注意力机制提高了模型的收敛速度并降低了字符错误率。实验结果表明，该模型在日语CSJ-500h和Magic-1600h数据集上的表现优于现有模型。

增强注意力机制的Citrinet语音识别模型