  The voice conversion task is to modify the speaker identity of continuous
speech while preserving the linguistic content. Generally, the naturalness and
similarity are two main metrics for evaluating the conversion quality, which
has been improved significantly in recent years. This paper presents the
HCCL-DKU entry for the fake audio generation task of the 2022 ICASSP ADD
challenge. We propose a novel ppg-based voice conversion model that adopts a
fully end-to-end structure. Experimental results show that the proposed method
outperforms other conversion models, including Tacotron-based and
Fastspeech-based models, on conversion quality and spoofing performance against
anti-spoofing systems. In addition, we investigate several post-processing
methods for better spoofing power. Finally, we achieve second place with a
deception success rate of 0.916 in the ADD challenge.
