Abstract: In today's fast-paced world, effective communication and collaboration are important, as meetings become the core for productive discussion and decision-making. Nevertheless, the manual transcription method continues to plague meetings and be a time-consuming error-prone torture. To overcome this challenge, our project, 'AutoMeet,' presents an innovative solution: a real-time automated meeting minutes generation system, including real-time speech detection, and a summarized meeting summary. AutoMeet operates live during the meeting. Our integrated system relies on the most advanced speech recognition systems to convert spoken words into transcribed text while maintaining the nuances and meaning of the conversation. It then uses text-to-speech technology to intelligently parse the notes into a transcript containing conclusions, key points, and content discussion. Automatic Meeting also includes real-time audio detection and recording of speakers participating in the conversation.
This further enhances the system's ability to capture and rate what the speaker is saying, even in a dynamic conversational environment. AutoMeet revolutionizes the traditional meeting recording process, streamlining these critical tasks, saving organizations time, and increasing productivity. Most importantly, it makes the outcomes of the meeting more effective and efficient, thus making the meeting more efficient, effective, and collaborative across the business and the environment. AutoMeet offers new features to simplify workflows, giving organizations cutting-edge tools to harness the true power of meetings.
Keywords: Automated meeting minutes generation, Meeting summarization, Speech recognition, Text summarization, Real-time speech detection, Artificial intelligence, Deep learning, Machine Learning, Natural Language Processing.
Cite:
Mohammed Rumaan, Salwa Imthiyaz Ahamed, Muhammed Sinan, Brinda Shetty, Mrs. Vasudha G Rao,"Automeet: AI-Powered Automated Meeting Transcripts", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13342.
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DOI:
10.17148/IJARCCE.2024.13342
