挑战用真实场景语音提升语音伪造生成与检测能力
WildSpoof Challenge Evaluation Plan
- 分生成与检测双赛道，用真实环境语音数据训练
- 推动从实验室数据转向真实世界应用的演进
- 适合语音安全、对抗攻防方向的研究者
WildSpoof挑战旨在推进真实场景语音在两项紧密关联的语音处理任务中的应用。挑战包含两个并行赛道：(1) 文本转语音（TTS）合成伪造语音；(2) 抗伪造自动说话人验证（SASV）检测伪造语音。组织方统一协调两赛道并定义数据规范，但参与者将其视为独立任务。主要目标为：(i) 推动在TTS和SASV中使用真实环境数据，突破传统干净可控数据集局限，面向真实应用场景；(ii) 促进伪造生成（TTS）与伪造检测（SASV）领域的跨社区协作，推动更集成、鲁棒且贴近现实的系统发展。
原文摘要 · Abstract (English)
The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthesis for generating spoofed speech, and (2) Spoofing-robust Automatic Speaker Verification (SASV) for detecting spoofed speech. While the organizers coordinate both tracks and define the data protocols, participants treat them as separate and independent tasks. The primary objectives of the challenge are: (i) to promote the use of in-the-wild data for both TTS and SASV, moving beyond conventional clean and controlled datasets and considering real-world scenarios; and (ii) to encourage interdisciplinary collaboration between the spoofing generation (TTS) and spoofing detection (SASV) communities, thereby fostering the development of more integrated, robust, and realistic systems.
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