Text style transfer task requires the model to transfer a sentence of one
style to another style while retaining its original content meaning, which is a
challenging problem that has long suffered from the shortage of parallel data.
In this paper, we first propose a semi-supervised text style transfer model
that combines the small-scale parallel data with the large-scale nonparallel
data. With these two types of training data, we introduce a projection function
between the latent space of different styles and design two constraints to
train it. We also introduce two other simple but effective semi-supervised
methods to compare with. To evaluate the performance of the proposed methods,
we build and release a novel style transfer dataset that alters sentences
between the style of ancient Chinese poem and the modern Chinese.

该研究提出一种半监督文本风格转换模型，将小规模平行数据与大规模非平行数据相结合，设计两个约束条件用于训练，引入了两种简单而有效的半监督方法，并构建并发布了一种新的风格转换数据集。

半监督文本风格转移：潜空间交叉投影

Semi-supervised Text Style Transfer: Cross Projection in Latent Space

We propose a nonparallel data-driven emotional speech conversion method. It
enables the transfer of emotion-related characteristics of a speech signal
while preserving the speaker's identity and linguistic content. Most existing
approaches require parallel data and time alignment, which is not available in
most real applications. We achieve nonparallel training based on an
unsupervised style transfer technique, which learns a translation model between
two distributions instead of a deterministic one-to-one mapping between paired
examples. The conversion model consists of an encoder and a decoder for each
emotion domain. We assume that the speech signal can be decomposed into an
emotion-invariant content code and an emotion-related style code in latent
space. Emotion conversion is performed by extracting and recombining the
content code of the source speech and the style code of the target emotion. We
tested our method on a nonparallel corpora with four emotions. Both subjective
and objective evaluations show the effectiveness of our approach.

本文提出了一种非并行数据驱动的情感语音转换方法，通过学习两个分布之间的翻译模型而实现非并行训练，该方法在音频内容编码与情感编码空间中进行情感转换，实现了保留说话人身份和语言特征的情感相关特征转移。

非平行情感语音转换

Nonparallel Emotional Speech Conversion

Although voice conversion (VC) algorithms have achieved remarkable success
along with the development of machine learning, superior performance is still
difficult to achieve when using nonparallel data. In this paper, we propose
using a cycle-consistent adversarial network (CycleGAN) for nonparallel
data-based VC training. A CycleGAN is a generative adversarial network (GAN)
originally developed for unpaired image-to-image translation. A subjective
evaluation of inter-gender conversion demonstrated that the proposed method
significantly outperformed a method based on the Merlin open source neural
network speech synthesis system (a parallel VC system adapted for our setup)
and a GAN-based parallel VC system. This is the first research to show that the
performance of a nonparallel VC method can exceed that of state-of-the-art
parallel VC methods.

本文提出了使用循环一致对抗网络（CycleGAN）进行非平行数据语音转换训练的方法，并通过主观评价证明其优于 Merlin 开源神经网络语音合成系统和基于 GAN 的平行 VC 系统。这是非平行 VC 方法表现超过最先进的平行 VC 方法的首次研究。