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Voice-based To-Do-List using Gen-AI

  • Unique Paper ID: 170232
  • Volume: 11
  • Issue: 6
  • PageNo: 3676-3686
  • Abstract:
  • The importance of meetings has become such an integral part of operations activities for a better vision of collaboration, decision-making, and solving problems. However, this leads to messy notes left for the participants and a long list of things to do in order to organize and prioritize. The management of the aftermaths of meetings, which comprises summarizing and assigning responsibilities, sometimes becomes the way to experience inefficiency in some aspects, missed deadlines, and poorly managed tasks. This paper seeks to voice a novel solution using advanced Gen-AI and NLP solutions to automatically generate to-do lists from the transcripts of meetings, thereby helping improve general post-meeting work processes and productivity. At its core are speech recognition and NLP models combined with large language models such as Groq, intended to convert the discussions of meetings into neatly organized tasks with defined responsibilities and deadlines. As such, the system does not use manual transcription and task allocations, which can be associated with human errors. This would save a few valuable minutes, as it will allow employees to spend this time on some more essential issues relative to their job. The core of the system is speech-to-text technology, which helps it capture all the spoken dialogue during a meeting and then translate it into texts.This text is then processed by the NLP component to extract key tasks, decisions, and other action items. The integration of LLMs ensures that the extracted information is synthesized into coherent, actionable to-do lists. Additionally, the system provides the capability to automatically dispatch these lists to relevant participants via email, further improving efficiency. The text then goes through the NLP component to help draw out the relevant tasks, decisions, and other action items. The ability provided by LLM in this work ensures that the extracted information is synthesized into coherent actionable to-do lists. Additionally, automatically sending those lists to relevant participants via email provides further capability for improvement in efficiency. This paper evaluates the effectiveness of the system in real-life by finding how it has performed in terms of accuracy of the task, user satisfaction and time-saving. Further, the paper has analyzed the broader impact of using AI-driven systems for managing tasks where those technologies improve the productivity of organizations through results where meetings are leading toward clearly defined and actionable outcomes. Enabling the automation of certain elements of the processes that follow a meeting, organizations are better able to deal with workload, create responsibility and aid better communication among the team members. The system is quite encouraging, but certain components still need to be worked on for example improving the noisy environment speech recognition and the complexities of the AI federal instructions system The present study also underlines what is shown in the literature, that they need to encourage future research and development of the system concerning the use of AI in everyday business activities and its enhancement. It is possible to state that these systems can change the paradigm of conducting meetings and organizing tasks in the organization to make it even more effective and efficient, as long as the development and improvement processes are cyclic.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 3676-3686

Voice-based To-Do-List using Gen-AI

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