1. Abstract

The AI Meeting Transcription and Action-Item Generator is an AI-powered application designed to convert meeting recordings into text transcripts, concise summaries, and actionable task lists. The system uses Speech-to-Text technology, Natural Language Processing (NLP), and Large Language Models (LLMs) to process conversations and identify important discussion points, decisions, assigned responsibilities, and deadlines. It can organize meeting content into structured sections, identify potential action items, and associate tasks with participants when the information is available in the transcript. The application provides a user-friendly dashboard for uploading recordings, reviewing transcripts, editing generated summaries, and exporting meeting reports. By automating repetitive documentation tasks, the system helps teams improve meeting productivity, follow-up, and collaboration. Human verification is supported to correct transcription errors and confirm task assignments before they are finalized.

2. Objectives

1. To convert meeting audio recordings into readable text transcripts.
2. To generate concise summaries of lengthy meetings.
3. To identify key discussion points and decisions.
4. To extract action items from meeting conversations.
5. To identify task owners and deadlines when explicitly stated.
6. To support speaker identification where the selected model permits it.
7. To organize meeting information into structured reports.
8. To reduce manual meeting-note preparation.
9. To enable transcript editing, searching, and report export.
10. To evaluate transcription accuracy and action-item extraction quality.

3. Existing System

Traditional meeting documentation relies on manual note-taking, human transcription, and manually assigned follow-up tasks. These approaches can be time-consuming and may result in missed decisions, incomplete notes, or unclear responsibilities. Basic transcription tools convert speech into text but may not automatically identify important decisions, summarize discussions, or extract actionable tasks.

Disadvantages

1. Manual note-taking can distract participants from discussions.
2. Long recordings take time to transcribe and review.
3. Important decisions may be missed or recorded inaccurately.
4. Action items and deadlines can remain unclear.
5. Speaker identification may be difficult in group meetings.
6. Meeting notes may be inconsistent across teams.
7. Manual follow-up tracking requires additional effort.

4. Proposed System

The proposed system combines automatic speech recognition, speaker diarization, and LLM-based text processing to generate structured meeting documentation. Users upload an audio or video recording, and the system extracts the audio and converts it into a transcript. Where supported, speaker diarization identifies different speakers and assigns labels to their speech segments.

An LLM then analyzes the transcript to produce a summary, key discussion points, decisions, and action items. Each action item can include a task description, assigned owner, deadline, and supporting transcript reference when available. The dashboard allows users to review and edit generated information before downloading the final report.

Key Features

1. Audio and video meeting upload.
2. Automatic speech-to-text transcription.
3. Speaker diarization where supported.
4. Meeting summary generation.
5. Key-topic and decision extraction.
6. Action-item and task-deadline identification.
7. Participant and task-owner extraction.
8. Timestamp-based transcript navigation.
9. Editable meeting notes and task lists.
10. Searchable transcript history.
11. PDF, DOCX, and TXT report export.
12. Optional integration with task-management tools.

5. Implementation Procedure

1. Collect meeting recordings: Prepare sample recordings in supported audio or video formats, ensuring appropriate permission to process participants' speech.

2. Develop the upload interface: Create a web interface that accepts recordings and displays processing status.

3. Extract audio: Use FFmpeg or a compatible library to extract audio from video files and convert it into a supported format.

4. Implement speech recognition: Integrate a Speech-to-Text model such as Whisper or a compatible cloud service to generate the transcript.

5. Implement speaker diarization: Use a compatible diarization model or service to separate speech segments by speaker when required.

6. Preprocess the transcript: Organize transcript segments, timestamps, speaker labels, and punctuation while preserving the original content.

7. Generate meeting summaries: Use an LLM to summarize the meeting into an overview, major discussion points, and decisions.

8. Extract action items: Prompt the LLM to identify tasks, task owners, and deadlines. Mark missing or ambiguous information as unassigned or unspecified rather than inventing details.

9. Link results to source text: Store supporting transcript excerpts and timestamps for important decisions and action items to make verification easier.

10. Build the dashboard: Display the transcript, summary, decisions, and action items in editable sections.

11. Export reports: Generate downloadable reports in formats such as PDF, DOCX, and TXT. Optionally provide task-list exports for project-management tools.

12. Test the system: Evaluate word error rate (WER), summary quality, action-item precision and recall, speaker-label accuracy, and processing time.

13. Deploy securely: Protect uploaded recordings, control access, manage retention periods, and secure any third-party AI API credentials.

6. Software Requirements

7. Hardware Requirements

Note: Processing long recordings or running speech-recognition and LLM models locally may require additional memory, storage, and GPU resources.

8. Advantages of the Project

1. Automated transcription: Converts meeting recordings into searchable text.
2. Time-saving summaries: Condenses lengthy discussions into key points.
3. Actionable follow-ups: Extracts tasks, responsibilities, and stated deadlines.
4. Decision tracking: Organizes important outcomes in a structured format.
5. Improved collaboration: Helps team members review meeting outcomes.
6. Searchable records: Makes it easier to locate information in previous meetings.
7. Editable results: Allows users to correct transcription and extraction errors.
8. Flexible reporting: Supports downloadable meeting documentation.
9. Integration potential: Can connect with calendars and project-management platforms.
10. Better accountability: Helps teams review and track agreed tasks.