Voice faking, driven primarily by recent advances in text-to-speech (TTS) synthesis technology, poses significant societal challenges. Currently, the prevailing assumption is that unaltered human speech can be considered genuine, while fake speech comes from TTS synthesis. We argue that this binary distinction is oversimplified. For instance, altered playback speeds can be used for malicious purposes, like in the 'Drunken Nancy Pelosi' incident. Similarly, editing of audio clips can be done ethically, e.g., for brevity or summarization in news reporting or podcasts, but editing can also create misleading narratives. In this paper, we propose a conceptual shift away from the binary paradigm of audio being either 'fake' or 'real'. Instead, our focus is on pinpointing 'voice edits', which encompass traditional modifications like filters and cuts, as well as TTS synthesis and VC systems. We delineate 6 categories and curate a new challenge dataset rooted in the M-AILABS corpus, for which we present baseline detection systems. And most importantly, we argue that merely categorizing audio as fake or real is a dangerous over-simplification that will fail to move the field of speech technology forward.

翻译：语音伪造——主要由近期文本转语音（TTS）合成技术的进步所驱动——带来了重大社会挑战。当前普遍假设是：未经改动的人类语音可视为真实语音，而伪造语音则来自TTS合成。我们认为这种二元区分过于简化。例如，改变播放速度可能被用于恶意目的，正如"酩酊大醉的南希·佩洛西"事件所示。类似地，音频剪辑的编辑既可能出于正当目的（如在新闻报道或播客中追求简洁或概括），也可能制造误导性叙事。本文提出概念性转变：摒弃将音频简单划分为"伪造"或"真实"的二元范式。我们转而聚焦于识别"语音编辑"——这涵盖传统修改手段（如滤波与剪切），以及TTS合成与语音转换（VC）系统。我们划分出六类语音编辑操作，基于M-AILABS语料库构建了全新的挑战数据集，并给出了基线检测系统。最重要的是，我们认为仅将音频归类为伪造或真实是一种危险的过度简化，这种思路无法推动语音技术领域的实质性发展。