YourTTS：面向人人的零样本多说话人TTS与零样本语音转换
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
AI总结：
该研究提出基于VITS改进的YourTTS模型，实现零样本多说话人TTS与语音转换的SOTA效果，支持低资源语言场景，且仅需不到1分钟语音即可微调适配新说话人。
摘要：
YourTTS将多语言方法的能力引入零样本多说话人TTS任务。该方法基于VITS模型构建，并针对零样本多说话人与多语言训练添加了若干新颖改进。我们在零样本多说话人TTS任务上取得了SOTA（当前最优）结果，在VCTK数据集上的零样本语音转换结果也与SOTA水平相当。此外，该方法在仅含单说话人数据集的目标语言中也取得了可观结果，为低资源语言下的零样本多说话人TTS与零样本语音转换系统开辟了可能。最后，用不到1分钟的语音即可微调YourTTS模型，在语音相似度上达到SOTA效果且质量合理，这对合成与训练阶段所见语音或录音特征差异极大的说话人语音而言十分重要。
英文摘要：
YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.
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。
