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.

本文介绍了我们的 MSXF TTS 系统，使用了端到端 TTS 系统，并在训练阶段添加了一个约束损失。我们还研究了语音速度和音量对欺诈的影响。我们的团队在 ADD 挑战赛上获得了第四名。

ICASSP 2022 ADD 挑战赛的 MSXF TTS 系统

The MSXF TTS System for ICASSP 2022 ADD Challenge

Recent years have seen rapid progress in identifying predefined relationship
between entity pairs using neural networks NNs. However, such models often make
predictions for each entity pair individually, thus often fail to solve the
inconsistency among different predictions, which can be characterized by
discrete relation constraints. These constraints are often defined over
combinations of entity-relation-entity triples, since there often lack of
explicitly well-defined type and cardinality requirements for the relations. In
this paper, we propose a unified framework to integrate relation constraints
with NNs by introducing a new loss term, ConstraintLoss. Particularly, we
develop two efficient methods to capture how well the local predictions from
multiple instance pairs satisfy the relation constraints. Experiments on both
English and Chinese datasets show that our approach can help NNs learn from
discrete relation constraints to reduce inconsistency among local predictions,
and outperform popular neural relation extraction NRE models even enhanced with
extra post-processing. Our source code and datasets will be released at
this https URL

本文提出了一种新的关系提取框架，引入了一个新的损失函数 ConstraintLoss，用于融合关系限制与神经网络，从而提高神经网络的学习效果，实验结果显示该方法比当前流行的 NRE 模型表现更好。