Contextual transcription and Summarization of audio using AI

The field of Natural Language Processing (NLP) has revolutionized the way human language interacts with computer systems. NLP applications span machine translation, information extraction, summarization, and question answering, driven by vast computational resources and big data methodologies. Despite these advancements, NLP tools haven't fully integrated with Internet of Things (IoT) devices, like audio recorders, hindering their accessibility and usability. This paper introduces an innovative solution: a method for audio transcription and contextual summarization using NLP, addressing this gap and enhancing comprehension. Our approach employs cutting-edge NLP techniques, including word embedding methods and knowledge-based graphs, to create a system that efficiently converts audio content into written text and generates coherent summaries. Unlike existing AI tools, our system's summaries are not only accurate but also rich and deep, providing insightful representations of the original content. This depth is achieved through advanced linguistic analysis, surpassing tools like ChatGPT. Furthermore, our system breaks language barriers, enabling multilingual data traversal, enhancing accessibility on a global scale. Our research methodology ensures the system's adherence to industry standards like Request for Comments (RFC) and Constrained Application Protocol (CoAP), guaranteeing interoperability and reliability. By incorporating knowledge-based graphs, our system comprehensively understands audio content, enhancing the accuracy of summarization. This approach addresses the unmet need for seamlessly integrating NLP with IoT devices, making the technology accessible to a broader audience.