基于伪孪生解耦网络的零样本口音转换
Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network
AI总结：
本文提出伪孪生解耦网络PSDN，实现零样本且无需参考语音的口音转换，在保留内容与说话人身份的同时显著提升目标口音强度。
摘要：
口音转换（AC）的目标是在保留内容和说话人身份的同时，将语音的口音转换为目标口音。口音转换支持多种应用，如语言学习、语音内容创作和数据增强。以往的方法在推理阶段依赖参考语音，或者无法保留说话人身份。为了解决这些问题，我们提出了一种零样本、无需参考语音的口音转换方法，能够将未见过的说话人的语音转换为目标口音。我们提出了伪孪生解耦网络（PSDN），用于将口音与内容表示解耦。实验结果表明，在外语转母语和母语转外语的双向转换中，我们的模型生成的语音样本口音强度远高于输入语音，并且自然度相当。
英文摘要：
The goal of accent conversion (AC) is to convert the accent of speech into the target accent while preserving the content and speaker identity. AC enables a variety of applications, such as language learning, speech content creation, and data augmentation. Previous methods rely on reference utterances in the inference phase or are unable to preserve speaker identity. To address these issues, we propose a zero-shot reference-free accent conversion method, which is able to convert unseen speakers' utterances into a target accent. Pseudo Siamese Disentanglement Network (PSDN) is proposed to disentangle the accent from the content representation. Experimental results show that our model generates speech samples with much higher accentedness than the input and comparable naturalness, on two-way conversion including foreign-to-native and native-to-foreign.
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。
