最优传输映射是优秀的语音转换器
Optimal Transport Maps are Good Voice Converters
AI总结：
本文探索最优传输框架用于语音转换，提出多种算法适配不同数据表示，在梅尔频谱图上实现FAD上界理论保证，并在WavLM潜在空间取得最先进结果。
摘要：
近年来，基于神经网络的计算最优传输映射方法已被有效应用于风格迁移问题。然而，这些方法在语音转换中的应用尚未得到充分探索。在我们的论文中，我们通过研究最优传输作为语音转换的框架来填补这一空白。我们提出了多种针对不同数据表示设计的最优传输算法，例如梅尔频谱图以及自监督语音模型的潜在表示。对于梅尔频谱图数据表示，我们在弗雷歇音频距离（FAD）方面取得了强劲的结果。这一性能与我们的理论分析一致，该分析表明我们的方法提供了目标分布与生成分布之间FAD的上界。在WavLM编码器的潜在空间内，我们取得了最先进的结果，并且即使参考说话人数据有限，也超越了现有方法。
英文摘要：
Recently, neural network-based methods for computing optimal transport maps have been effectively applied to style transfer problems. However, the application of these methods to voice conversion is underexplored. In our paper, we fill this gap by investigating optimal transport as a framework for voice conversion. We present a variety of optimal transport algorithms designed for different data representations, such as mel-spectrograms and latent representation of self-supervised speech models. For the mel-spectogram data representation, we achieve strong results in terms of Frechet Audio Distance (FAD). This performance is consistent with our theoretical analysis, which suggests that our method provides an upper bound on the FAD between the target and generated distributions. Within the latent space of the WavLM encoder, we achived state-of-the-art results and outperformed existing methods even with limited reference speaker data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
