In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator/detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed - achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.

翻译：在语音生成模型快速发展的背景下，确保音频真实性以应对语音克隆风险已成为迫切需求。我们提出AudioSeal——首个专为AI生成语音本地化检测设计的音频水印技术。AudioSeal采用生成器/检测器架构，通过联合训练定位损失函数实现样本级的本地化水印检测，并引入基于听觉掩蔽效应的新型感知损失，从而获得更优的不可感知性。在鲁棒性方面，该技术对实际音频操作具有出色抵抗能力；基于自动评估与人工评价指标，其在不可感知性方面均达到业界领先水平。此外，AudioSeal配备高速单次检测器，检测速度较现有模型提升两个数量级，特别适用于大规模实时应用场景。