## 4 Results and Discussion
### The SEB-ASR System
#### 4.2.1 Operational Workflow  
The SEB-ASR system workflow, as depicted in Figure 5, begins with taking an audio input and using the VOSK Mandarin ASR model to recognize and analyze the audio, assigning metadata to each word, including start and end times, confidence levels, and recognition results. Each recognized segment is evaluated based on a predefined confidence threshold of 0.9. The segments with a confidence level above the threshold are marked with a \"+\" symbol, indicating they are part of the main Mandarin content. In contrast, segments with a confidence level below the threshold are marked with a \"-\" symbol, considered potential English segments misrecognized by the Mandarin model. The Mandarin list is then updated with the assigned symbols for each segment. As the VOSK Mandarin ASR is monolingual, English words recognized by the Mandarin model are often recognized as multiple Mandarin parts, some of which have high confidence and are mistakenly treated as Mandarin. To address this, the system checks if the end time of one word matches the start time of the next word, indicating consecutive pronunciation, which may signify a single English word. If the start and end times of words align, the audio segments are evaluated and marked with \"-\". These segments are then processed using an English ASR model to generate supplementary English transcriptions. The English transcriptions are integrated with the main Mandarin content based on their start and end times, resulting in  
Figure 4: Histograms of the distribution of MER results for all data in Dataset A, Dataset B, and Dataset All when tested using different methods. (**a**) shows the CER results for each dataset when using the VOSK speech recognition toolkit. (**b**) shows the MER results for each dataset when using the SEB-ASR system. The numbers in the graph show how many data samples are in the corresponding MER range using the VOSK and SEB-ASR systems. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset A**} & \multicolumn{2}{c}{**Dataset B**} & \multicolumn{2}{c}{**Dataset All**} & \multicolumn{2}{c}{**YouTube**} \\ \cline{2-9}  & **VOSK** & **SEB-ASR** & **VOSK** & **SEB-ASR** & **VOSK** & **SEB-ASR** & **VOSK** & **SEB-ASR** \\ \hline Maximum (\%) & 79.81 & 20.64 & 88.00 & 20.93 & 89.93 & 24.00 & 99.57 & 28.91 \\ Minimum (\%) & 31.89 & 0.00 & 28.00 & 0.00 & 30.03 & 0.00 & 43.22 & 0.00 \\ Average (\%) & 54.93 & 9.36 & 52.08 & 7.56 & 56.49 & **11.50** & 65.22 & **12.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the VOSK monolingual ASR and SEB-ASR systems for bilingual speech recognition: the maximum, minimum, and average MER results across various Mandarin–English mixed datasets. a combined Mandarin-English speech recognition output. The system performs an MER analysis to evaluate its performance by comparing the recognized results with a reference text, ultimately producing a bilingual recognized result. The Mandarin characters in the article are also expressed as pinyin, the official Romanization system for Standard Mandarin Chinese using the Latin alphabet, along with their meaning in the parentheses. To explain the system in more detail, consider an audio data from our dataset with the phrase \"?id \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Assigned Symbol** & **Start Time** & **End Time** & **Reassigned Symbol** & **Confidence** & \
\begin{tabular}{c} **Recognized** \\ **Word** \\ \end{tabular} &
\begin{tabular}{c} **Original** \\ **Audio Text** \\ \end{tabular} \\ \hline - & 42.42 & 42.75 & - & 0.2720 & \(\
exists\) (\(\forall\)_fi_i_i) & \\ + & 42.75 & 42.90 & - & 0.9928 & \(\
exists\) (\(\exists\)_hi_) & efficiency \\ + & 42.90 & 43.32 & - & 0.9035 & \(\
exists\) (\(\exists\)_hi_) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reassignment (refined evaluation) of symbols (True or False) in speech recognition results from the SEB-ASR system based on segment continuity.