## 5 Conclusions  
In this study, we have developed and evaluated the SEB-ASR system, designed to handle mixed-language audio recognition, specifically for Mandarin and English. By leveraging the VOSK monolingual ASR toolkit via an API interface, we have created an innovative solution for accurately recognizing and integrating mixed-language content. The SEB-ASR system has demonstrated a remarkable reduction in the MER, achieving an average of less than 12% for bilingual recognition for our mixed dataset, significantly outperforming the VOSK baseline. The YouTube dataset, which included real-world examples of Mandarin-English mixed conversations, further validated our system robustness, achieving an average MER of less than 13% across all tested datasets. This indicates the accuracy of the SEB-ASR system is high not only on our created datasets but also on naturally occurring mixed-language speech. These results emphasize the significant advancements our SEB-ASR system brings to bilingual speech recognition, showcasing its potential for broader applications. The structured approach within the framework of self-evaluation mixed-language speech recognition can be adapted for other language pairs, paving the way for developing multilingual ASR systems. By preparing mixed-language datasets and applying our methodology, similar improvements can be achieved in trilingual or even more complex multilingual speech recognition tasks without requiring intricate  
Figure 6: Comparison of MER/CER results. The distributions of MER/CER results for each dataset are shown when using the VOSK monolingual ASR toolkit and the SEB-ASR system, respectively. (**a**) represents the distribution of “Dataset A”, (**b**) represents the distribution of “Dataset B”, (**c**) represents the distribution of “Dataset All”, and (**d**) represents the distribution of data from “YouTube” audios.  
training processes. In conclusion, the SEB-ASR system marks a significant step forward in bilingual and mixed-language ASR. Its robust performance and adaptability demonstrate the potential for implementing this technology across various languages, facilitating efficient and accurate multilingual speech recognition in an increasingly globalized and interconnected world.  
Conceptualization, X.H., C.-C.Y. and M.-C.L.; methodology, X.H., K.A. and M.-C.L.; software, X.H., K.A., Z.H. and M.-C.L.; validation, X.H., K.A., C.-C.Y., Z.H., C.-Y.H., H.-Y.H. and M.-C.L.; formal analysis, X.H., C.-C.Y. and M.-C.L.; investigation, X.H., C.-C.Y., Z.H., C.-Y.H. and M.-C.L.; resources, C.-C.Y., C.-Y.H., H.-Y.H. and M.-C.L.; data curation, X.H., K.A. and M.-C.L.; writing--original draft preparation, X.H. and K.A.; writing--review and editing, X.H., K.A., C.-C.Y., H.-Y.H. and M.-C.L.; visualization, X.H., C.-C.Y. and M.-C.L.; supervision, M.-C.L.; project administration, M.-C.L.; funding acquisition, C.-C.Y. and M.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company Lifetree Telemed Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.