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LLM-powered system corrects speaker errors in real-time meetings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an LLM-assisted system designed to correct speaker attribution errors in real-time during meetings. The system leverages automatic speech recognition (ASR) and diarization, then uses LLM-generated summaries to help users pinpoint and fix mistakes. It incorporates user feedback to update transcripts and enroll new speakers, with mechanisms to precisely identify intended corrections. Evaluations on the AMI headset test set showed significant reductions in DER and speaker substitution errors compared to a baseline system. AI

IMPACT This system could improve the accuracy and usability of meeting transcripts, making them more valuable for analysis and record-keeping.

RANK_REASON This is a research paper detailing a novel system for in-meeting speaker correction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-powered system corrects speaker errors in real-time meetings

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This is a research paper detailing a novel system for in-meeting speaker correction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xinlu He, Yiwen Guan, Badrivishal Paurana, Pitipat Kongsomjit, Zilin Dai, Jacob Whitehill ·

    Interactive In-Meeting Speaker Correction with Human Feedback

    arXiv:2509.18377v2 Announce Type: replace Abstract: Most automatic speech processing systems operate in ``open loop'' mode without user feedback about who said what, yet human-in-the-loop workflows can potentially enable higher accuracy. We propose an LLM-assisted in-meeting spea…

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