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Interactive In-Meeting Speaker Correction with Human Feedback

arXiv — cs.CL•Friday, May 29, 2026 at 4:00:00 AM
PositiveArtificial Intelligence
  • What Happened

    A new interactive in-meeting speaker correction system has been proposed, leveraging large language models (LLMs) to enhance automatic speech recognition (ASR) accuracy by allowing users to provide corrective feedback on speaker attribution errors. This system integrates streaming ASR and diarization, presenting LLM-generated summaries to assist users in identifying and correcting errors in real-time.

  • Why It Matters

    The development of this system is significant as it aims to improve the reliability of speaker attribution in meetings, which is crucial for accurate documentation and understanding of discussions. By incorporating user feedback, the system not only enhances accuracy but also fosters a more collaborative environment during meetings.

  • The Bigger Picture

    This innovation reflects a broader trend in AI towards human-in-the-loop systems, where user interaction is essential for refining outputs. Similar approaches are emerging in various domains, such as automated scoring and interactive speech recognition, highlighting the growing recognition of the importance of user feedback in enhancing AI performance and reliability.

— via World Pulse Now AI Editorial System
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