This paper proposes a simple and robust zero-shot voice conversion system
with a cycle structure and mel-spectrogram pre-processing. Previous works
suffer from information loss and poor synthesis quality due to their reliance
on a carefully designed bottleneck structure. Moreover, models relying solely
on self-reconstruction loss struggled with reproducing different speakers'
voices. To address these issues, we suggested a cycle-consistency loss that
considers conversion back and forth between target and source speakers.
Additionally, stacked random-shuffled mel-spectrograms and a label smoothing
method are utilized during speaker encoder training to extract a
time-independent global speaker representation from speech, which is the key to
a zero-shot conversion. Our model outperforms existing state-of-the-art results
in both subjective and objective evaluations. Furthermore, it facilitates
cross-lingual voice conversions and enhances the quality of synthesized speech.

提出了一个简单而强大的零样本语音转换系统，使用了循环结构和梅尔频谱预处理，通过循环一致性损失考虑目标和源说话者之间的转换，同时利用堆叠的随机排序梅尔频谱和标签平滑方法在说话者编码器训练过程中提取不受时间约束的全局说话者表示，模型在主观和客观评估中表现优于现有的最先进结果，并可促进跨语言语音转换并提高合成语音的质量。

AutoCycle-VC: 以瓶颈独立为目标的零样本跨语言语音转换

AutoCycle-VC: Towards Bottleneck-Independent Zero-Shot Cross-Lingual  Voice Conversion

While the theoretical analysis of evolutionary algorithms (EAs) has made
significant progress for pseudo-Boolean optimization problems in the last 25
years, only sporadic theoretical results exist on how EAs solve
permutation-based problems.
To overcome the lack of permutation-based benchmark problems, we propose a
general way to transfer the classic pseudo-Boolean benchmarks into benchmarks
defined on sets of permutations. We then conduct a rigorous runtime analysis of
the permutation-based $(1+1)$ EA proposed by Scharnow, Tinnefeld, and Wegener
(2004) on the analogues of the LeadingOnes and Jump benchmarks. The latter
shows that, different from bit-strings, it is not only the Hamming distance
that determines how difficult it is to mutate a permutation $\sigma$ into
another one $\tau$, but also the precise cycle structure of $\sigma \tau^{-1}$.
For this reason, we also regard the more symmetric scramble mutation operator.
We observe that it not only leads to simpler proofs, but also reduces the
runtime on jump functions with odd jump size by a factor of $\Theta(n)$.
Finally, we show that a heavy-tailed version of the scramble operator, as in
the bit-string case, leads to a speed-up of order $m^{\Theta(m)}$ on jump
functions with jump size $m$. A short empirical analysis confirms these
findings, but also reveals that small implementation details like the rate of
void mutations can make an important difference.

该论文提出了一种将基于经典伪布尔基准问题的算法推广到排列基准问题上的方法，并对旋转置换的基准问题进行了分析，发现排列的循环结构决定了变异的难度，并提出使用更对称的 Scramble 变异操作器和重尾 Scramble 操作器来加速算法运行，并进行了实证分析。

基于置换的进化算法的运行时分析

Runtime Analysis for Permutation-based Evolutionary Algorithms

While the theoretical analysis of evolutionary algorithms (EAs) has made
significant progress for pseudo-Boolean optimization problems in the last 25
years, only sporadic theoretical results exist on how EAs solve
permutation-based problems.
To overcome the lack of permutation-based benchmark problems, we propose a
general way to transfer the classic pseudo-Boolean benchmarks into benchmarks
defined on sets of permutations. We then conduct a rigorous runtime analysis of
the permutation-based $(1+1)$ EA proposed by Scharnow, Tinnefeld, and Wegener
(2004) on the analogues of the \textsc{LeadingOnes} and \textsc{Jump}
benchmarks. The latter shows that, different from bit-strings, it is not only
the Hamming distance that determines how difficult it is to mutate a
permutation $\sigma$ into another one $\tau$, but also the precise cycle
structure of $\sigma \tau^{-1}$. For this reason, we also regard the more
symmetric scramble mutation operator. We observe that it not only leads to
simpler proofs, but also reduces the runtime on jump functions with odd jump
size by a factor of $\Theta(n)$. Finally, we show that a heavy-tailed version
of the scramble operator, as in the bit-string case, leads to a speed-up of
order $m^{\Theta(m)}$ on jump functions with jump size~$m$.%

本研究提出了将经典的伪布尔基准转化为置换集基准的一般方法，分析了置换基础 $(1+1)$EA 在 LeadingOnes 和 Jump 基准的模拟上的运行时间，旨在研究置换问题。