首个用大模型分析语音欺骗的框架，能识别多种伪造手法及其语义影响。
HoliAntiSpoof: Audio LLM for Holistic Speech Anti-Spoofing
- 将语音伪造检测转为文本生成任务，统一分析伪造方法与语义影响
- 在新基准DailyTalkEdit上表现优于传统方法，跨域泛化能力更强
- 适合语音安全、可解释性研究者，支持对抗攻击分析
语音合成与编辑技术的进步使语音欺骗愈发严峻。现有方法多将欺骗视为二分类问题，忽视了不同欺骗手段对多个耦合语音属性及语义的影响。本文提出HoliAntiSpoof，首个面向全面语音反欺骗分析的音频大语言模型（ALLM）框架。该框架将欺骗分析重构为统一的文本生成任务，实现对欺骗方法、受影响语音属性及其语义效应的联合推理。为支持语义层面分析，我们构建了DailyTalkEdit——一个模拟真实对话篡改的新基准，提供语义影响标注。大量实验表明，HoliAntiSpoof在多种设置下均优于传统基线；初步结果还显示，在上下文学习下其跨域泛化能力进一步提升。这些发现表明，音频大语言模型不仅能提升语音欺骗检测性能，还可实现对欺骗行为及其语义影响的可解释分析，推动更可信、可解释的语音安全发展。数据与代码已公开。
原文摘要 · Abstract (English)
Recent advances in speech synthesis and editing have made speech spoofing increasingly challenging. However, most existing methods treat spoofing as binary classification, overlooking that diverse spoofing techniques manipulate multiple, coupled speech attributes and their semantic effects. In this paper, we introduce HoliAntiSpoof, the first audio large language model (ALLM) framework for holistic speech anti-spoofing analysis. HoliAntiSpoof reformulates spoofing analysis as a unified text generation task, enabling joint reasoning over spoofing methods, affected speech attributes, and their semantic impacts. To support semantic-level analysis, we introduce DailyTalkEdit, a new anti-spoofing benchmark that simulates realistic conversational manipulations and provides annotations of semantic influence. Extensive experiments demonstrate that HoliAntiSpoof outperforms conventional baselines across multiple settings, while preliminary results show that in-context learning further improves out-of-domain generalization. These findings indicate that ALLMs not only enhance speech spoofing detection performance but also enable interpretable analysis of spoofing behaviors and their semantic effects, pointing towards more trustworthy and explainable speech security. Data and code are publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。