## 4 Results
### Speech Recognition Results  
Table 4 provides a comparison of CER between SAformerNet, Conformer, and other ASR models. The results show that SAformerNet has improved compared to previously published systems. Moreover, the SAformerNet-S model achieved competitive results of 5.50%/5.76% on the AISHELL-1 dataset with only 13.4 M parameters. However, this is still a departure from the original work, as it benefited from training with bigger volumes and a more comprehensive set of resources. Furthermore, the experimental results show that the feasibility of the SAformerNet speech recognition model is verified, and a recognition rate of 5.92% is achieved by training and testing on the Zhuang language dataset. Additionally, as shown in Figure 11, complementary ablation studies are performed using Conformer-S as a baseline model to better understand the improvements brought about by the various improvement methods. The study initially examines the impact of downsampling on accuracy, multiply add operations, and training time reduction. It demonstrates that downsampling effectively decreases temporal redundancy in the feature representation of speech frames. The utilization of SE-Net into the convolution module improves accuracy, shows that feature recalibration can be adapted to the requirements of the network, compensates for missing information during downsampling, and better utilizes MHSA for global downsampling operations. The training time and inference time of the network is further reduced by introducing grouped attention in the network, showing that the use of grouped attention can reduce the complexity of the model. The architecture of the model was evaluated using Vivado 2018.2 on a Xilinx XCZU9 EG FPGA, with a batch size set to 1. The utilization report is shown in Table 5. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Translation Model**} & \multirow{2}{*}{**Data Types**} & **ASPEC-JC** & **OPUS JA-ZH** \\  & & **BLEU Scores** & **BLEU Scores** \\ \hline Chinese-to-Japanese & Original data & 34.24 & 34.20 \\ Japanese-to-Chinese & Original data & 32.19 & 33.41 \\ Chinese-to-Japanese & Processed data & 35.16 & 35.45 \\ Japanese-to-Chinese & Processed data & 33.02 & 33.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Translation effects when different data are used for training and tests. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Translation Model**} & \multirow{2}{*}{**Retranslation**} & **ASPEC-JC** & **OPUS JA-ZH** \\  & & **Strategies** & **BLEU Scores** & **BLEU Scores** \\ \hline Chinese-to-Japanese & No filtering mechanism & 35.97 & 36.12 \\ Japanese-to-Chinese & No filtering mechanism & 34.31 & 34.36 \\ Chinese-to-Japanese & With filtering mechanism & 37.51 & 37.74 \\ Japanese-to-Chinese & With filtering mechanism & 35.82 & 35.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance under different retranslation strategies. SAformerNet improves the training speed by 26% and the average inference time by 29%, compared to the baseline model. In the real-world deployment use of the model with the FPGA acceleration, the overall average improvement in the result-to-terminal time for speech recognition is 20%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Model Architecture**} & \multirow{3}{*}{**Model Type**} & \multicolumn{4}{c}{**CER (\%)**} \\ \cline{3-6}  & & & **AISHELL-1** & & **Zhuang Language** & **Params (M)** \\ \cline{3-6}  & & & **Dev** & **Test** & **Test** & \\ \hline LAS + SpecAugm & Seq2 Seq & 8.63 & 11.32 & 13.2 & - \\ Conformer-S & CTC & 5.62 & 5.95 & 7.36 & 13.0 \\ Eff. Conformer-S & RNN-T & 5.68 & 6.03 & 7.49 & 10.3 \\ SAformerNet-S (ours) & CTC & 5.69 & 6.13 & 6.21 & 13.4 \\ w/o grouped Att & CTC & 5.50 & 5.76 & 5.92 & 13.4 \\ \hline Conformer-M & CTC & 5.40 & 5.67 & 6.99 & 30.5 \\ Eff. Conformer-M & RNN-T & 5.43 & 5.81 & 7.11 & 30.7 \\ SAformerNet m (ours) & CTC & 5.52 & 6.01 & 6.11 & 33.4 \\ w/o grouped Att & CTC & 5.36 & 5.58 & 5.67 & 33.4 \\ \hline \hline \end{tabular}
\end{table}