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Audio LLMs unify speech editing detection and localization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to unify speech editing detection and content localization using Audio Large Language Models. This approach addresses limitations in existing methods, particularly for deletion-type edits, by reformulating the task as a structured text generation problem. The framework incorporates a large-scale bilingual dataset called AiEdit, which includes a wider variety of realistic editing operations. AI

IMPACT Enhances the ability to detect sophisticated audio manipulations, crucial for content verification and security.

RANK_REASON The cluster contains an academic paper detailing a new method and dataset for speech editing detection and localization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Audio LLMs unify speech editing detection and localization

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The cluster contains an academic paper detailing a new method and dataset for speech editing detection and localization. [lever_c_demoted from research: ic=1 ai=1.0]
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161 days old
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COVERAGE [1]

  1. 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 ·

    Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs

    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…

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