论文标题
添加2022：第一个音频深度合成检测挑战
ADD 2022: the First Audio Deep Synthesis Detection Challenge
论文作者
论文摘要
Audio DeepFake检测是一个新兴的话题，已包含在2021年ASVSPOOF中。但是，最近的共享任务并未涵盖许多现实生活和具有挑战性的情况。第一个音频深度合成检测挑战（ADD）是有动机来填补空白的。添加2022包括三个曲目：低质量的假音频检测（LF），部分假音频检测（PF）和音频假游戏（FG）。 LF轨道着重于用各种现实世界的噪音处理善意和完全假的话语。PF轨道旨在将部分假音频与真实的音频区分开。 FG轨道是一款竞争游戏，其中包括两个任务：音频生成任务和一个音频伪造的检测任务。在本文中，我们描述了数据集，评估指标和协议。我们还报告了主要发现，这些发现反映了Audio DeepFake检测任务的最新进展。
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.
