标题： 多语言语音深度伪造源追踪：首个基准测试 显示英文标题

标题： Multilingual Source Tracing of Speech Deepfakes: A First Benchmark

摘要： 最近生成式AI的进展使得仅需几秒钟的音频就能创建出听起来自然的深度伪造语音变得更加容易。 尽管这些工具支持有益的应用，但它们也引发了严重的问题，因为可以生成许多语言的令人信服的虚假语音。 当前的研究主要集中在检测虚假语音上，但对于追踪生成虚假语音的源模型却关注较少。 本文介绍了第一个多语言语音深度伪造源追踪的基准，涵盖了单语和跨语言场景。 我们比较研究了DSP和SSL建模；探讨了在不同语言上微调的SSL表示如何影响跨语言泛化性能；并评估了对未见过的语言和说话人的泛化能力。 我们的研究结果提供了在训练和推理语言不同时识别语音生成模型的挑战方面的首次全面见解。 数据集、协议和代码可在 https://github.com/xuanxixi/Multilingual-Source-Tracing 获取。 显示英文摘要

摘要： Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages. Current research has largely focused on detecting fake speech, but little attention has been given to tracing the source models used to generate it. This paper introduces the first benchmark for multilingual speech deepfake source tracing, covering both mono- and cross-lingual scenarios. We comparatively investigate DSP- and SSL-based modeling; examine how SSL representations fine-tuned on different languages impact cross-lingual generalization performance; and evaluate generalization to unseen languages and speakers. Our findings offer the first comprehensive insights into the challenges of identifying speech generation models when training and inference languages differ. The dataset, protocol and code are available at https://github.com/xuanxixi/Multilingual-Source-Tracing.