新基准VoxENES 2026用5万多样本和10种现代语音合成方法，测了8个检测器，最佳EER才28.98%，说明现有检测器对LLM语音基本失效，做语音安全的朋友可以看看。
VoxENES 2026是一个双语（英语和西班牙语）基准，包含53,628个音频样本，使用10种现代语音合成方法生成，在10种标准化后处理条件下评估。8个预训练检测器未经微调测试，最佳模型总体EER为28.98%，大多数接近或低于随机水平。结果凸显当前检测器依赖脆弱伪影，难以泛化到LLM驱动的TTS和语音转换。
VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion
Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-processing conditions. We bridge this gap by introducing VoxENES 2026, a bilingual (English and Spanish) benchmark of 53,628 audio samples generated using 10 contemporary speech synthesis methods and evaluated under 10 standardized post-processing conditions. Using VoxENES 2026, we benchmark eight pretrained detectors without fine-tuning and observe substantial performance degradation: the best model achieves 28.98\% EER overall, while most perform near or below random chance across modern generators and perturbations. Our results highlight the reliance on brittle artifacts in current detectors and establish VoxENES 2026 as a practical testbed for developing robust audio spoofing countermeasures.