English(EN) Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs

音频大语言模型统一语音编辑检测与本地化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员开发了一个新的框架，利用音频大语言模型统一语音编辑检测与内容本地化。该方法通过将任务重塑为结构化文本生成问题，解决了现有方法在删除类编辑方面的局限性。该框架包含一个名为AiEdit的大规模双语数据集，其中包含更多种类的真实编辑操作。 AI

影响增强了检测复杂音频操纵的能力，这对于内容验证和安全至关重要。

排序理由该集群包含一篇学术论文，详细介绍了用于语音编辑检测和本地化的新方法和数据集。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了用于语音编辑检测和本地化的新方法和数据集。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Jun Xue, Yi Chai, Yanzhen Ren, Jinshen He, Zhiqiang Tang, Zhuolin Yi, Yihuan Huang, Yuankun Xie, Yujie Chen · 2026-05-26 04:00

通过先验增强音频大模型统一语音编辑检测与内容本地化

arXiv:2601.21463v3 Announce Type: replace-cross Abstract: Existing speech editing detection (SED) datasets are predominantly constructed using manual splicing or limited editing operations, resulting in restricted diversity and poor coverage of realistic editing scenarios. Meanwh…