Abstract
With the advancements in AI speech synthesis, it is easier than ever before to generate realistic audio in a target voice. One only needs a few seconds of reference audio from the target, quite literally putting words in the target person's mouth. This imposes a new set of forensics-related challenges on speech-based authentication systems, videoconferencing, and audio-visual broadcasting platforms, where we want to detect synthetic speech. At the same time, leveraging AI speech synthesis can enhance the different modes of communication through features such as low-bandwidth communication and audio enhancements - leading to ever-increasing legitimate use-cases of synthetic audio. In this case, we want to verify if the synthesized voice is actually spoken by the user. This will require a mechanism to verify whether a given synthetic audio is driven by an authorized identity, or not. We term this task audio avatar fingerprinting. As a step towards audio forensics in these new and emerging situations, we analyze and extend an off-the-shelf speaker verification model developed outside of forensics context for the task of fake speech detection and audio avatar fingerprinting, the first experimentation of its kind. Furthermore, we observe that no existing dataset allows for the novel task of verifying the authorized use of synthetic audio - a limitation which we address by introducing a new speech forensics dataset for this novel task.
Abstract (translated)
随着AI语音合成技术的进步，生成目标声音的真实音频变得前所未有的容易。只需几秒的目标参考音频，就能让目标人物说出特定话语。这给基于语音的身份验证系统、视频会议和音视频广播平台带来了全新的法证相关挑战——我们需要在这些场景中检测合成语音。与此同时，利用AI语音合成也能通过低带宽通信和音频增强等功能提升不同通信模式，导致合成音频的合法使用场景不断增加。在这种情况下，我们需要验证合成语音是否确实由用户发出，这就需要一种机制来判断给定合成音频是否由授权身份驱动。我们将此任务称为“音频化身指纹”。作为面向这些新兴场景的音频法证研究一步，我们分析并扩展了一个原本非法证领域的现成说话人验证模型，用于伪造语音检测和音频化身指纹任务——这是该领域的首次尝试。此外，我们观察到目前尚无数据集支持合成音频授权验证这一新型任务，为此我们专门构建了一个针对该任务的新语音法证数据集以解决这一局限。
URL
https://arxiv.org/abs/2603.20165
