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。

一种新的声音真实性方法

A New Approach to Voice Authenticity

This paper presents a novel design of neural network system for fine-grained
style modeling, transfer and prediction in expressive text-to-speech (TTS)
synthesis. Fine-grained modeling is realized by extracting style embeddings
from the mel-spectrograms of phone-level speech segments. Collaborative
learning and adversarial learning strategies are applied in order to achieve
effective disentanglement of content and style factors in speech and alleviate
the "content leakage" problem in style modeling. The proposed system can be
used for varying-content speech style transfer in the single-speaker scenario.
The results of objective and subjective evaluation show that our system
performs better than other fine-grained speech style transfer models,
especially in the aspect of content preservation. By incorporating a style
predictor, the proposed system can also be used for text-to-speech synthesis.
Audio samples are provided for system demonstration
this https URL .

该论文提出了一种新型的神经网络系统，用于情感文本转语音合成中的细粒度风格建模、转移和预测，该系统通过从音素水平的语音段的梅尔光谱图中提取风格嵌入来实现细粒度建模，并应用协作学习和对抗学习策略来实现内容和风格因素的有效区分。该系统不仅可以用于单个说话者情况下的不同内容的语音风格转移，还可以用于文本到语音合成，并在内容保留方面表现更好。