计算机科学
二元分类
二进制数
数据科学
多媒体
人工智能
数学
支持向量机
算术
作者
Jiangyan Yi,Jianhua Tao,Ruibo Fu,Xinrui Yan,Chenglong Wang,Tao Wang,Chu Yuan Zhang,Mengjie Zhang,Yan Zhao,Yang Ren,Lei Xu,Jianfeng Zhou,Hongchen Gu,Zhengqi Wen,Shuquan Liang,Zheng Lian,Songlin Nie,Haizhou Li
出处
期刊:Cornell University - arXiv
日期:2023-05-23
标识
DOI:10.48550/arxiv.2305.13774
摘要
Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.
科研通智能强力驱动
Strongly Powered by AbleSci AI