Abstract
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.
Abstract (translated)
近期在语音合成和编辑领域的进展使得语音伪造变得更加困难。然而，大多数现有的方法将语音伪造视为二元分类问题，忽略了各种伪造技术会操纵多个相互关联的语音属性及其语义效果这一事实。在这篇论文中，我们介绍了HoliAntiSpoof，这是第一个用于全面语音防伪分析的音频大型语言模型（ALLM）框架。HoliAntiSpoof将伪造分析重构为一个统一的文字生成任务，使对伪造方法、受影响的语音属性及它们语义影响的同时推理成为可能。为了支持语义级别的分析，我们引入了DailyTalkEdit，一个新的用于防伪基准测试的数据集，它模拟现实中的对话操作，并提供了关于语义影响的注释。广泛的实验表明，HoliAntiSpoof在多个设置下超越了传统的基线方法，而初步结果显示，在上下文学习的帮助下，模型在外域泛化方面也有显著提升。这些发现表明，ALLMs不仅能提高语音伪造检测的表现力，还能使对伪造行为及其语义效果的解释性分析成为可能，从而朝着更值得信赖和可解释的语音安全迈进了一步。数据和代码现已公开提供。
URL
https://arxiv.org/abs/2602.04535
