## 5 Results
### Model Fusion Experiment  
To further optimize the models, we employed a weighted fusion approach. For each individual model, we conducted a comparative experiment between arithmetic averaging and weighted averaging under the same configuration environment. The comparison of the single model fusion test results for the three structures is presented in Tables 5-7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{5}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Unified\_Conformer \\ (Arithmetic mean) \\ \end{tabular} } & attention & 4.55\% & 10.12\% & 4.93\% & 10.31\% \\  & ctc\_greedy\_search & 4.68\% & 12.05\% & 4.88\% & 12.16\% \\  & ctc\_prefix\_beam\_search & 4.63\% & 12.03\% & 4.84\% & 12.09\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Unified\_Conformer \\ (weighted mean) \\ \end{tabular} } & attention & 4.56\% & 10.12\% & 4.93\% & 10.32\% \\  & ctc\_greedy\_search & 4.67\% & 12.05\% & 4.87\% & 12.17\% \\ \cline{1-1}  & ctc\_prefix\_beam\_search & 4.63\% & 12.02\% & 4.83\% & 12.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Unified Conformer model fusion test results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{5}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} U2++\_Conformer \\ (Arithmetic mean) \\ \end{tabular} } & attention & 4.41\% & 10.59\% & 4.87\% & 10.83\% \\  & ctc\_greedy\_search & 5.08\% & 12.38\% & 5.26\% & 12.59\% \\  & ctc\_prefix\_beam\_search & 5.03\% & 12.34\% & 5.20\% & 12.48\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} U2++\_Conformer \\ (weighted mean) \\ \end{tabular} } & attention & 4.39\% & 10.54\% & 4.81\% & 10.79\% \\  & ctc\_greedy\_search & 5.05\% & 12.37\% & 5.27\% & 12.60\% \\ \cline{1-1}  & ctc\_prefix\_beam\_search & 5.01\% & 12.32\% & 5.20\% & 12.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: U2++ Conformer model fusion test results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{5}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Conformer \\ (Arithmetic mean) \\ \end{tabular} } & attention & 7.10\% & 16.30\% & 7.23\% & 16.75\% \\  & ctc\_greedy\_search & 8.83\% & 18.46\% & 9.19\% & 18.77\% \\  & ctc\_prefix\_beam\_search & 8.70\% & 18.39\% & 9.06\% & 18.73\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Conformer \\ (weighted mean) \\ \end{tabular} } & attention & 7.00\% & 16.26\% & 7.18\% & 16.72\% \\  & ctc\_greedy\_search & 8.83\% & 18.41\% & 9.16\% & 18.74\% \\ \cline{1-1}  & ctc\_prefix\_beam\_search & 8.69\% & 18.35\% & 9.06\% & 18.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Conformer model fusion test results. Tables 5-7 present a comparison of recognition performance between arithmetic averaging fusion and weighted averaging fusion within the same model. The comparison reveals that using weighted fusion provides certain improvements across different data environments and decoding algorithms compared to arithmetic averaging fusion. For example, in the Conformer model with the attention mechanism decoding algorithm, the WER in the clean environment decreases from 7.10% to 7.00% on the Dev-Clean set, and from 7.23% to 7.18% on the Test-Clean set. In the noisy environment, the WER decreases from 16.30% to 16.26% on the Dev-Other set, and from 16.75% to 16.72% on the Test-Other set. Similar optimizations are also observed in the Unified Conformer and U2++ Conformer models. These results indicate that model fusion using loss values as weights can effectively improve recognition accuracy, particularly in complex noisy environments.