## 4 Results and Discussion
### The SEB-ASR System
#### 4.2.2 Performance Evaluation  
Figure 4 shows the distribution of the MER results for each dataset using the VOSK monolingual ASR toolkit and the SEB-ASR system. The VOSK recognition results exhibit a significant concentration of higher CER values, particularly in the 40-60% range, with 737 entries in Dataset B and 5,639 entries in Dataset All. Dataset A displays a wider range, with 1,080 entries falling within the 40-70% CER range. The result of the YouTube dataset also shows higher CER values in the 50-70% range, with 693 entries. In contrast, the SEB-ASR distribution shifts towards lower MER ranges, with Dataset All peaking in the 0-10% MER range, accounting for 4,139 entries. The SEB-ASR results for Dataset A, Dataset B, and the YouTube dataset similarly follow this trend, peaking in the 0-10% MER range, with 628, 730, and 631 entries, respectively. The histograms of MER distributions across Dataset A, Dataset B, Dataset All, and the YouTube dataset, as shown in Figure 6, clearly indicate a substantial improvement in recognition accuracy when using the SEB-ASR system compared to the monolingual VOSK ASR. The results consolidated in Table 3 demonstrate that the SEB-ASR system significantly outperforms the monolingual VOSK. Specifically, for Dataset A, the SEB-ASR system reduced the maximum MER by approximately 3.87 times, from 79.81% with VOSK to 20.64%. For Dataset B, the SEB-ASR system lowered the maximum MER by approximately 4.21 times, decreasing from 88.00% with VOSK to 20.93%. For Dataset All, the reduction was approximately 3.75 times, with the maximum MER decreasing from 89.93% with VOSK to 24.00%. There is a significant performance change from the higher MER results of VOSK to the lower MER results of SEB-ASR, indicating the substantial improvement in bilingual recognition, as shown in Figures 6a-c, respectively, which illustrate the distributions of MER results for Dataset A, Dataset B, and Dataset All using both VOSK and SEB-ASR systems. Regarding the average MER, the SEB-ASR system consistently outperformed the monolingual VOSK one across all datasets. For Dataset A, there was an average recognition improvement of 5.87 times, with the average MER dropping from 54.93% with VOSK to 9.36% with SEB-ASR. The test of Dataset B shows an improvement in recognition by 6.89 times, with the average MER falling from 52.08% to 7.56%. For Dataset All, there was a 4.91 times improvement, with the average MER decreasing from 56.49% with VOSK to 11.50%. These reductions highlight the enhanced performance and accuracy of the SEB-ASR system across diverse datasets. The YouTube audio files have also been tested with the SEB-ASR system, and the results are comprehensively presented in Figure 6d. The  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Case** & **Text** & **S\({}_{\mathbf{C}}\)** & **I\({}_{\mathbf{C}}\)** & **D\({}_{\mathbf{C}}\)** & **S\({}_{\mathbf{W}}\)** & **I\({}_{\mathbf{W}}\)** & **D\({}_{\mathbf{W}}\)** & **N\({}_{\mathbf{M}}\)** & **MER** \\ \hline Recognized text & \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\)\(\hat{\texttt{m}}\) (_2 ie ge_) idea \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\) (_fei chdng_) & 0 & 3 & 0 & 1 & 0 & 0 & 15 & 26.66\% \\ without & perfect \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\)\(\hat{\texttt{m}}\) (_v0 mteh_) work \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\) (_xih shie_) & & & & & & & & \\ Recognized text & \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\)\(\hat{\texttt{m}}\) (_2 ie ge_) idea \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\) (_fei chdng_) & 0 & 0 & 0 & 0 & 0 & 0 & 15 & 0.00\% \\ with reassigned & perfect \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\)\(\hat{\texttt{m}}\) (_v0 mteh_) work \(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\)\(\hat{\texttt{x}}\) (_xih yao_ _t_maximum MER value was 28.91%, the minimum was 0.00%, and the average was 12.78%. The average MER of the SEB-ASR system was less than 13% across all tested datasets, while in contrast, that of the monolingual VOSK ASR approached \(\sim\)65%.