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

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. Meanwhile, current SED methods rely heavily on frame-level supervision to detect observable acoustic anomalies, which fundamentally limi