用于ICASSP 2022 ADD挑战赛的TTS系统
The MSXF TTS System for ICASSP 2022 ADD Challenge
摘要：
本文介绍了我们的MSXF TTS系统，用于2022年音频深度合成检测（ADD）挑战赛的任务3.1。我们使用一个端到端的文本到语音转换系统，并在训练阶段加入一个约束损失。端到端TTS系统是VITS，预训练自监督模型是wav2vec 2.0。并探讨了语速和音量对欺骗效果的影响。语音越快意味着音频中的无声部分越少，就越容易欺骗检测器。我们还发现，体积越小，欺骗能力越好，尽管我们对提交的体积进行了归一化。我们的队伍被确定为C2，我们在挑战赛中获得了第四名。
英文摘要：
This paper presents our MSXF TTS system for Task 3.1 of the Audio Deep Synthesis Detection (ADD) Challenge 2022. We use an end to end text to speech system, and add a constraint loss to the system when training stage. The end to end TTS system is VITS, and the pre-training self-supervised model is wav2vec 2.0. And we also explore the influence of the speech speed and volume in spoofing. The faster speech means the less the silence part in audio, the easier to fool the detector. We also find the smaller the volume, the better spoofing ability, though we normalize volume for submission. Our team is identified as C2, and we got the fourth place in the 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。
