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The voice conversion task is to modify the speaker identity of continuousspeech while preserving the linguistic content. Generally, the naturalness andsimilarity are two main metrics for evaluating the conversion quality, whichhas been improved significantly in recent years. This paper presents theHCCL-DKU entry for the fake audio generation task of the 2022 ICASSP ADDchallenge. We propose a novel ppg-based voice conversion model that adopts afully end-to-end structure. Experimental results show that the proposed methodoutperforms other conversion models, including Tacotron-based andFastspeech-based models, on conversion quality and spoofing performance againstanti-spoofing systems. In addition, we investigate several post-processingmethods for better spoofing power. Finally, we achieve second place with adeception success rate of 0.916 in the ADD challenge.

The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge