## 4 Results and Discussion
### VOSK Monolingual ASR Toolkit
#### 4.1.2 Performance Evaluation  
The performance evaluation of the algorithms was performed on an Ubuntu system version 20.04, and all the code was written in Python-3.8. The efficacy of the VOSK ASR  
Figure 2: Workflow of the API for speech-to-text processing. The diagram illustrates the interaction between the client, API, and server. The client sends a speech request to the API, which determines the type of request and forwards it to the VOSK server for processing. The server returns the text result to the API, which then converts it to an HTTP response for the client to accept.  
toolkit was assessed using the publicly available open-sourced dataset from Common Voice [56], which provides free speech data in multiple languages. Specifically, we independently evaluated VOSK's performance in recognizing English and Mandarin languages, and the findings are presented in Figure 3 and Table 2. The results reveal that the VOSK speech recognition toolkit exhibits an average WER of 8.43% for English ASR and a CER of 9.04% for Mandarin one. These findings underscore the high level of proficiency of the VOSK toolkit in executing monolingual ASR tasks.  
As the Mandarin model is unable to recognize the English components, calculating the average error rate for the English parts using the Mandarin model is not feasible. Hence, in Equation (3) the values of \(S_{W}\), \(D_{W}\), and \(I_{W}\) are neglected. In this context, the CER used for evaluating Mandarin is equivalent to the MER for assessing mixed speech. Therefore, to maintain consistency, the evaluation metric will be CER. As listed in Table 3, when analyzing the bilingual datasets using the VOSK Mandarin model, the result reveals considerable performance fluctuations. For Dataset A and Dataset B, the VOSK model exhibits maximum CERs of 79.81% and 88.00%, minimum CERs of 31.89% and 28.00%, and average CERs of 54.93% and 52.08%, respectively. The combined Dataset All resulted in a maximum CER of 89.93%, a minimum CER of 30.03%, and an average CER of 56.49%, presenting the highest maximum CER among all datasets analyzed, reflecting challenges in mixed data recognition. Figure 4a illustrates the distribution of CER results for each dataset using the VOSK monolingual ASR toolkit. The YouTube dataset achieved the highest CER across all datasets, with a maximum of 99.57%, a minimum of 43.22%, and an average of 65.22%, indicating significant recognition difficulties for bilingual datasets. These findings demonstrate that, while VOSK excels in recognizing a single language, it shows limited  
\begin{table}
\begin{tabular}{l|c|c} \hline \hline  & **English** & **Mandarin** \\ \hline Maximum (\%) & 22.80 & 25.32 \\ Minimum (\%) & 0.00 & 0.00 \\ Average (\%) & 8.43 & 9.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the VOSK monolingual ASR: the maximum, minimum, and average word error rate (WER) and character error rate (CER) in recognizing English and Mandarin Common Voice datasets.  
Figure 3: Distributions of error rate results for a single-language recognition by the VOSK monolingual ASR toolkit, (**a**) English WER recognition results, and (**b**) Mandarin CER recognition results. The horizontal axis represents the range of error rates, and the vertical axis represents the number of data points distributed in each interval.  
effectiveness in handling mixed-speech recognition tasks, especially when some English words are included in a Mandarin conversation.