## 1 Introduction  
Automatic speech recognition (ASR), commonly known as speech recognition, is a groundbreaking field within artificial intelligence (AI) that empowers computers to comprehend and respond to spoken language. Its vital role in our daily lives is revolutionizing human-computer interaction, making it more natural and efficient. ASR eliminates the need for traditional input methods like keyboards, enabling hands-free operation on mobile devices and improving accessibility and safety in applications like autonomous driving. This technology fosters inclusivity by providing an alternative means of interaction for individuals with physical disabilities and those facing challengeswith conventional input methods. Moreover, it facilitates bilingual communication, boosts productivity through quick transcription, powers voice-activated assistants like Siri, Google Assistant, Bixbi, Clova, Alexa, and more, and plays a crucial role in automated customer service and healthcare innovations [1]. As we witness ongoing advancements, ASR continues to reshape our relationship with technology, offering a seamless and intuitive interface that enriches our daily activities [2,3]. The history of ASR spans decades, beginning with Bell Labs' \"Audrey\" system in 1952, which recognized spoken numbers. Major advancements followed, such as the development of hidden Markov models (HMMs) in the 1970s and the more recent impact of deep learning and neural networks, transforming ASR systems into sophisticated tools today [4, 5, 6]. This article focuses on mixed-language speech, which involves the use of more than one language within an utterance. Mixed-language communication is quite common all over the world, for example, between Mandarin and English [7,8], French and Algerian [9], Spanish and English [10], and Ethio and Semitic [11]. Mandarin-English mixed-speech recognition is particularly common and popular in East Asia. Despite linguists studying bilingual speech for more than half a century, it is only in the past two decades, due to advancements in speech technology, that mixed-language speech recognition has received significant attention [8,12]. In particular, research on Mandarin-English mixed speech has gained momentum over the past decade [13, 14, 15]. The current trajectory of research in this domain gravitates towards themes like data assembly, modeling of articulatory units, addressing data sparsity, and holistic end-to-end solutions [16]. While advancements in speech recognition technology have made significant strides toward accurate mixed-language ASR, considerable challenges remain, particularly in creating comprehensive datasets and robust models capable of handling the complexity of mixed-language speech, which is discussed in detail in Section 2. Our study presents a new method for mixed-language ASR, focusing on bilingual speech recognition results. The algorithms examined in this article are designed for Mandarin-English mixed-language speech recognition to capture the essence of a speaker toggling between two languages within the same utterance. Our methodology utilizes the VOSK monolingual ASR toolkit without acoustic modeling for the initial recognition step. We have employed language-specific models for different target languages and curated a comprehensive dataset consisting of over 10,000 speech samples to evaluate the effectiveness of our proposed system. Throughout our testing, we have continuously refined the components of the system, comparing the baseline performance of the VOSK monolingual speech recognition toolkit with the outcomes from our enhanced system. Our investigation indicates that our method simplifies and improves speech recognition, thereby taking full advantage of the VOSK toolkit's [17] exceptional capabilities in monolingual recognition. This scalable framework offers an innovative solution for bilingual speech recognition, applicable to a diverse range of language pairs beyond Mandarin-English.