构建首个俄语语音反欺骗数据集，评估模型在真实环境下的泛化与鲁棒性。
Evaluating Generalization and Robustness in Russian Anti-Spoofing: The RuASD Initiative
- 基于37个俄语语音合成系统生成伪造语音，结合多源真实语音构建数据集。
- 模拟房间混响、噪声、音乐和编码器变换等真实场景干扰，实现可复现测试。
- 适用于评估语音反欺骗模型在复杂部署环境中的实际表现，适合安全与语音识别研究者。
RuASD（俄语反欺骗数据集）是一个专用于俄语语音反欺骗的可复现基准，旨在评估模型在域内区分能力及对部署风格分布偏移的鲁棒性。该数据集融合了利用37个现代俄语语音合成（TTS）与语音克隆系统生成的大量伪造语音子集，以及从多个异构开源俄语语音语料库中筛选的真实语音子集，支持跨多种数据来源的系统性评估。为可控且可复现地模拟典型传播与信道效应，数据集包含可配置的平台与传输失真模拟，涵盖房间混响、加性噪声/音乐及多种语音编码器转码，通过统一处理链实现。我们对一系列公开可用的反欺骗对策进行了基准测试，覆盖轻量级监督架构、图注意力模型、自监督学习（SSL）检测器及大规模预训练系统，并报告了在干净与模拟条件下的参考结果，以刻画模型在现实扰动流程下的鲁棒性。数据集已公开发布于Hugging Face与ModelScope。
原文摘要 · Abstract (English)
RuASD (Russian AntiSpoofing Dataset) is a dedicated, reproducible benchmark for Russian-language speech anti-spoofing designed to evaluate both in-domain discrimination and robustness to deployment-style distribution shifts. It combines a large spoof subset synthesized using 37 modern Russian-capable TTS and voice-cloning systems with a bona fide subset curated from multiple heterogeneous open Russian speech corpora, enabling systematic evaluation across diverse data sources. To emulate typical dissemination and channel effects in a controlled and reproducible manner, RuASD includes configurable simulations of platform and transmission distortions, including room reverberation, additive noise/music, and a range of speech-codec transcodings implemented via a unified processing chain. We benchmark a diverse set of publicly available anti-spoofing countermeasures spanning lightweight supervised architectures, graph-attention models, SSL-based detectors, and large-scale pretrained systems, and report reference results on both clean and simulated conditions to characterize robustness under realistic perturbation pipelines. The dataset is publickly available at \href{https://huggingface.co/datasets/MTUCI/RuASD}{\underline{Hugging Face}} and \href{https://modelscope.cn/datasets/lab260/RuASD}{\underline{ModelScope}}.
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