## 4 Results and Discussion  
### VOSK Monolingual ASR Toolkit  
VOSK is an open-source Python toolkit for both offline and online speech recognition [17], built on top of the Kaldi framework [55]. It currently supports models for more than  
Figure 1: Dataset composition structure diagram: The diagram shows the name of each folder, the number of audio files it contains, and the size of the folder. Dataset A contains a total of 1,110 audio files (2.0 GB) and is divided into three folders: File 1A contains 10 audio files (6.2 MB), File 2A contains 100 audio files (122.4 MB), and File 3A contains 1,000 audio files (1.8 GB). Dataset B contains a total of 1,110 audio files (2.2 GB) and is divided into three folders: File 1B contains 10 audio files (6.9 MB), File 2B contains 100 audio files (138.0 MB), and File 3B contains 1,000 audio files (2.1 GB). Dataset All includes a total of 8,420 audio files (16.2 GB) and is divided into three folders: File 1 All contains 20 audio files (13.0 MB), File 2 All contains 400 audio files (520.8 MB), and File 3 All contains 8,000 audio files (15.6 GB). The combined set consists of 20 audio files from Person A and Person B. The total dataset comprises 10,640 audio files (21.8 GB).  
20 languages, including English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish, Uzbek, Korean, Breton, Gujarati, Tajik, and Telugu. These models offer a variety of functionalities such as speaker identification, streaming APIs, and flexible vocabulary configuration. Although the accuracy of these models is not yet fully optimized, the toolkit delivers competitive performance and continues to improve with ongoing developments. The lightweight and modular design of VOSK makes it suitable for integration in a range of applications, from mobile devices to embedded systems. This combination of accessibility, support of many languages, and extendability positions VOSK as a valuable tool for research and practical applications in ASR, particularly in settings where an offline recognition is critical.  
As depicted in Figure 2, we have developed an API that facilitates real-time speech recognition of stored audio data in multiple languages, including English and Mandarin. APIs enable communication between different software programs by defining rules and protocols, allowing developers to access and utilize the functionalities and data of diverse applications or services. They play a pivotal role in modern software development. VOSK employs machine learning models to transcribe spoken words into written text, rooted in the Kaldi speech recognition infrastructure. We have formulated an API interface capable of dynamically invoking language models tailored to specific user needs. This design transcends the limitations of supporting only Chinese and English, encompassing all languages integrated into the VOSK monolingual ASR toolkit. This approach maximizes the efficacy of VOSK's advanced single-language recognition capabilities, enhancing its versatility across a broad linguistic landscape. For developers, VOSK offers a user-friendly interface, simplifying the integration of speech recognition into software applications. Furthermore, its open-source framework empowers developers to customize and extend its functionality to meet diverse requirements.