  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.
