Emotional voice conversion (EVC) aims to convert the emotion of speech from
one state to another while preserving the linguistic content and speaker
identity. In this paper, we study the disentanglement and recomposition of
emotional elements in speech through variational autoencoding Wasserstein
generative adversarial network (VAW-GAN). We propose a speaker-dependent EVC
framework based on VAW-GAN, that includes two VAW-GAN pipelines, one for
spectrum conversion, and another for prosody conversion. We train a spectral
encoder that disentangles emotion and prosody (F0) information from spectral
features; we also train a prosodic encoder that disentangles emotion modulation
of prosody (affective prosody) from linguistic prosody. At run-time, the
decoder of spectral VAW-GAN is conditioned on the output of prosodic VAW-GAN.
The vocoder takes the converted spectral and prosodic features to generate the
target emotional speech. Experiments validate the effectiveness of our proposed
method in both objective and subjective evaluations.

本篇研究通过变分自编码 Wasserstein 生成对抗网络（VAW-GAN）研究情感音频的解耦和重组，提出一种基于 VAW-GAN 的发言者依赖型情感语音转换框架，包括两个 VAW-GAN 管道，一个用于频谱转换，另一个用于韵律转换，在客观和主观评估中验证了我们提出的方法的有效性。

VAW-GAN: 一种用于语音情感元素解耦和重组的方法

VAW-GAN for Disentanglement and Recomposition of Emotional Elements in  Speech

Speaker identity is one of the important characteristics of human speech. In
voice conversion, we change the speaker identity from one to another, while
keeping the linguistic content unchanged. Voice conversion involves multiple
speech processing techniques, such as speech analysis, spectral conversion,
prosody conversion, speaker characterization, and vocoding. With the recent
advances in theory and practice, we are now able to produce human-like voice
quality with high speaker similarity. In this paper, we provide a comprehensive
overview of the state-of-the-art of voice conversion techniques and their
performance evaluation methods from the statistical approaches to deep
learning, and discuss their promise and limitations. We will also report the
recent Voice Conversion Challenges (VCC), the performance of the current state
of technology, and provide a summary of the available resources for voice
conversion research.

本文给出了语音转换技术及其性能评估方法的最新进展概述，并讨论了这些技术的优劣。同时还报告了最近语音转换挑战的表现和提供了可用于语音转换研究的资源概述。

语音转换及其挑战：从统计建模到深度学习的概述

An Overview of Voice Conversion and its Challenges: From Statistical  Modeling to Deep Learning

We propose a flexible framework for spectral conversion (SC) that facilitates
training with unaligned corpora. Many SC frameworks require parallel corpora,
phonetic alignments, or explicit frame-wise correspondence for learning
conversion functions or for synthesizing a target spectrum with the aid of
alignments. However, these requirements gravely limit the scope of practical
applications of SC due to scarcity or even unavailability of parallel corpora.
We propose an SC framework based on variational auto-encoder which enables us
to exploit non-parallel corpora. The framework comprises an encoder that learns
speaker-independent phonetic representations and a decoder that learns to
reconstruct the designated speaker. It removes the requirement of parallel
corpora or phonetic alignments to train a spectral conversion system. We report
objective and subjective evaluations to validate our proposed method and
compare it to SC methods that have access to aligned corpora.

本文提出了基于可变自动编码器的光谱转换 SC 框架，以利用非平行语料库进行训练，消除与使用平行语料库或音素对齐有关的限制，包括编码器和解码器来学习光谱转换系统，通过客观和主观评价验证了该方法的有效性并与其他 SC 方法进行了比较。