## 4 Experiments
### Overall Results  
The comprehensive results presented in Table 1 illustrate the evaluation outcomes of the proposed SPMSpell model alongside seven baseline models. The F1 scores for sentence-level detection and correction, as well as character-level detection and correction, are reported on the SIGHAN 13/14/15 test datasets, with bold font highlighting the best-performing results. The results showcase the superiority of the SPMSpell model across all test sets, both at the sentence and character levels. Particularly noteworthy is the exceptional performance on the SIGHAN 15 test set, where SPMSpell outperforms all baseline models, affirming the model's efficacy. When compared to models incorporating speech and visual features, such as REALISE, SPMSpell achieves a notable improvement in detection/correction F1 by 0.9%, 0.9%, 4.3%, and 2.9% on SIGHAN 14/15, respectively. This underscores the effectiveness of ChineseBERT-based encoders in mitigating the mismatch phenomenon arising from the fusion of multiple feature information.  
It is noteworthy that the observed improvement in recall and F1, while substantial, is accompanied by a disparity in accuracy compared to other baseline models. This discrepancy may stem from the limitation of the training data volume. To address this, the introduction of a new public dataset could augment the training volume and potentially bridge the existing gap. Nevertheless, it is essential to highlight that the model's performance remains competitive, particularly in comparison to other methods incorporating speech information for this task.  
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Detection-Level**} & \multicolumn{3}{c}{**Correction-Level**} & \multicolumn{3}{c}{**Char-Level**} \\  & & **D-P** & **D-R** & **D-F** & **C-P** & **C-R** & **C-F** & **D-F** & **C-F** \\ \hline \multirow{8}{*}{SIGHAN13} & GAD & 85.7 & 79.5 & 82.5 & 84.9 & 78.7 & 81.5 & 87.6 & 93.5 \\  & DCN & 86.8 & 79.6 & 83.0 & 84.7 & 77.7 & 81.0 & 85.2 & 86.4 \\  & REALISE & 88.6 & 82.5 & 85.4 & 87.2 & 81.2 & 84.1 & 86.1 & 88.4 \\  & uChecker & 75.4 & 73.4 & 74.4 & 72.6 & 70.8 & 71.7 & - & - \\  & MDCSpell & **89.1** & 78.3 & 83.4 & **87.5** & 76.8 & 81.8 & - & - \\  & ECOPE & 87.2 & 81.7 & 84.4 & 86.1 & 80.6 & 83.3 & - & - \\  & UMRSpell & 83.0 & 73.6 & 78.0 & 80.0 & 71.0 & 75.2 & 84.9 & **96.4** \\ \cline{2-11}  & **SPMSpell (our)** & 87.7 & **83.7** & **85.6** & 86.9 & **82.8** & **84.6** & **92.1** & 95.3 \\ \hline \multirow{8}{*}{SIGHAN14} & GAD & 66.6 & 71.8 & 69.1 & 65.0 & 70.1 & 67.5 & **82.9** & 87.6 \\  & DCN & 67.4 & 70.4 & 68.9 & 65.9 & 68.7 & 67.2 & 78.9 & 86.2 \\ \cline{1-1}  & REALISE & 67.8 & 71.5 & 69.6 & 66.3 & 70.0 & 68.1 & 78.5 & 80.1 \\ \cline{1-1}  & uChecker & 61.7 & 61.5 & 61.6 & 57.6 & 57.5 & 57.6 & - & - \\ \cline{1-1}  & MDCSpell & **70.2** & 68.8 & 69.5 & **69.0** & 67.7 & 68.3 & - & - \\ \cline{1-1}  & ECOPE & 65.8 & 69.0 & 67.4 & 63.7 & 66.9 & 65.3 & - & - \\ \cline{1-1}  & UMRSpell & 69.0 & 56.6 & 62.2 & 63.9 & 57.2 & 60.4 & 73.2 & **93.3** \\ \cline{1-1} \cline{2-11}  & **SPMSpell (our)** & 68.6 & **73.5** & **70.5** & 67.0 & **71.2** & **69.0** & 81.5 & 89.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of each model on test sets.