用于2022年ICASSP ADD挑战赛假音频生成任务的HCCL-DKU系统
The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge
摘要：
语音转换的任务是修改连续语音的说话人身份，同时保持语言内容。通常，自然度和相似度是评价转换质量的两个主要指标，近年来这两个指标得到了显著的提高。本文介绍了2022 ICASSP ADD挑战赛的伪音频生成任务的HCCL-DKU条目。我们提出了一个新的基于ppg的语音转换模型，采用了完全端到端的结构。实验结果表明，该方法在转换质量和欺骗性能上优于其他转换模型，包括基于Tacotron和基于Fastspeech的转换模型。此外，我们研究了几种后处理方法，以提高欺骗能力。最后，我们在ADD挑战中以0.916的欺骗成功率获得第二名。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
