Traditional voice conversion methods rely on parallel recordings of multiple
speakers pronouncing the same sentences. For real-world applications however,
parallel data is rarely available. We propose MelGAN-VC, a voice conversion
method that relies on non-parallel speech data and is able to convert audio
signals of arbitrary length from a source voice to a target voice. We firstly
compute spectrograms from waveform data and then perform a domain translation
using a Generative Adversarial Network (GAN) architecture. An additional
siamese network helps preserving speech information in the translation process,
without sacrificing the ability to flexibly model the style of the target
speaker. We test our framework with a dataset of clean speech recordings, as
well as with a collection of noisy real-world speech examples. Finally, we
apply the same method to perform music style transfer, translating arbitrarily
long music samples from one genre to another, and showing that our framework is
flexible and can be used for audio manipulation applications different from
voice conversion.

MelGAN-VC 使用 GAN 架构、siamese 网络等对非平行语音数据进行语音转换，不仅能精确地将源语音转换为目标语音，还能保留语音信息和灵活地建模目标说话者的风格，同时还能用于音乐风格转换等音频处理应用。

MelGAN-VC: 基于频谱的任意长度样本的语音转换和音频风格转换

MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long  samples using Spectrograms

Text attribute transfer using non-parallel data requires methods that can
perform disentanglement of content and linguistic attributes. In this work, we
propose multiple improvements over the existing approaches that enable the
encoder-decoder framework to cope with the text attribute transfer from
non-parallel data. We perform experiments on the sentiment transfer task using
two datasets. For both datasets, our proposed method outperforms a strong
baseline in two of the three employed evaluation metrics.

本文提出对现有方法进行改进，以应对来自非平行数据的文本属性转移，并在情感转移任务中使用两个数据集进行实验，结果显示在三个评估指标中，我们的方法都优于强基准线。

改进的神经文本属性转移与非平行数据

Improved Neural Text Attribute Transfer with Non-parallel Data

Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other
domains, such as computer vision, mainly because of the lack of parallel data
and principle evaluation metrics. In this paper, we propose to learn style
transfer with non-parallel data. We explore two models to achieve this goal,
and the key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks. We also
propose novel evaluation metrics which measure two aspects of style transfer:
transfer strength and content preservation. We access our models and the
evaluation metrics on two tasks: paper-news title transfer, and
positive-negative review transfer. Results show that the proposed content
preservation metric is highly correlate to human judgments, and the proposed
models are able to generate sentences with higher style transfer strength and
similar content preservation score comparing to auto-encoder.

本文提出使用对抗网络以学习分离内容表示和风格表示的方法来解决自然语言处理中的样式转移问题，并提出了新的评估指标来测量样式转移的转移强度和内容保留。作者在文章 - 新闻标题转移和正面 - 负面评论转移两个任务上评估了模型和指标，结果表明，所提出的模型的样式转移强度和内容保留得分比自动编码器更高，内容保留度指标与人类判断高度相关。