This paper introduces our system designed for Track 2, which focuses on locating manipulated regions, in the second Audio Deepfake Detection Challenge (ADD 2023). Our approach involves the utilization of multiple detection systems to identify splicing regions and determine their authenticity. Specifically, we train and integrate two frame-level systems: one for boundary detection and the other for deepfake detection. Additionally, we employ a third VAE model trained exclusively on genuine data to determine the authenticity of a given audio clip. Through the fusion of these three systems, our top-performing solution for the ADD challenge achieves an impressive 82.23% sentence accuracy and an F1 score of 60.66%. This results in a final ADD score of 0.6713, securing the first rank in Track 2 of ADD 2023.

翻译：本文介绍了我们为第二届音频深度伪造检测挑战赛（ADD 2023）中聚焦操控区域定位的轨道2所设计的系统。该方法采用多个检测系统来识别拼接区域并判定其真实性。具体而言，我们训练并集成了两个帧级系统：一个用于边界检测，另一个用于深度伪造检测。此外，我们还利用一个仅在真实数据上训练的第三个VAE模型，以判定给定音频片段的真实性。通过融合这三个系统，我们在ADD挑战中的最优方案实现了令人瞩目的82.23%句子准确率与60.66%的F1分数，最终得到0.6713的ADD得分，斩获ADD 2023轨道2第一名的成绩。