Abstract
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{this https URL}{\underline{Hugging Face}} and \href{this https URL}{\underline{ModelScope}}.
Abstract (translated)
RuASD（俄语反欺骗数据集）是一个专门设计、可复现的俄语语音反欺骗基准，用于评估模型在域内判别能力以及对实际部署场景分布偏移的鲁棒性。该数据集结合了通过37种现代俄语TTS及声音克隆系统合成的大规模欺骗样本，以及从多个异构公开俄语语音语料库中精心筛选的真实语音样本，从而实现对不同数据来源的系统化评估。为在可控且可复现的条件下模拟典型传播与信道效应，RuASD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/音乐干扰，以及通过统一处理链实现的一系列语音编解码转码操作。我们针对一系列公开可用的反欺骗对策进行了基准测试，涵盖轻量级监督架构、图注意力模型、基于自监督学习的检测器及大规模预训练系统，并在纯净条件与模拟条件下报告了参考结果，以刻画其在真实扰动流程下的鲁棒性。该数据集已在\href{this https URL}{\underline{Hugging Face}}和\href{this https URL}{\underline{ModelScope}}公开。
URL
https://arxiv.org/abs/2604.02374
