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.

声音伪造，驱动主要是最近文本到语音合成技术的进展，对社会提出了重大挑战。我们主张从传统的二元范式即声音是 “真实” 或 “伪造” 进行观念转变，而将重点放在定位 “声音编辑” 上，这包括传统的修改、过滤和剪辑，以及文本到语音合成和语音变换系统。我们提出了六个类别，并策划了一个基于 M-AILABS 语料库的新挑战数据集，并提供了基线检测系统。我们认为，仅将声音分类为真实或伪造是一种危险的过度简化，将不能推动语音技术领域的发展。