English(EN) Interactive In-Meeting Speaker Correction with Human Feedback

LLM驱动的系统实时纠正会议中的说话人错误

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

研究人员开发了一个LLM辅助系统，旨在实时纠正会议中的说话人归属错误。该系统利用自动语音识别（ASR）和说话人分割技术，然后使用LLM生成的摘要帮助用户精确定位和修复错误。它整合了用户反馈来更新文字记录并注册新说话人，并包含精确识别预期纠错的机制。在AMI头戴设备测试集上的评估显示，与基线系统相比，DER和说话人替换错误显著减少。 AI

影响该系统可以提高会议记录的准确性和可用性，使其在分析和存档方面更有价值。

排序理由这是一篇研究论文，详细介绍了一种新颖的会议中说话人纠错系统。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 这是一篇研究论文，详细介绍了一种新颖的会议中说话人纠错系统。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Xinlu He, Yiwen Guan, Badrivishal Paurana, Pitipat Kongsomjit, Zilin Dai, Jacob Whitehill · 2026-05-29 04:00

交互式会议中发言人纠错与人类反馈

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…

本文如何被排名

报道来源 [1]

交互式会议中发言人纠错与人类反馈

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