StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
该论文链接原文地址 发表于Interspeech 2019 总体概括： 1.使用了很多损失函数 对抗 分类（区别人） 身份（进一步对目标的映射）循环一致性损失 ，这些损失都来源于STARGA 以及Cycle-GAN。 2.一对多 其它很多是一对一 该方法是一对多 此处对应损失函数分类和身份 循环一致性损失 3.非平行数据 同时论文中表明为何使用非平行数据，因为平行数据很多还需要人工的
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相关文章
- 1. StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
- 2. 2019trans--Sequence-to-Sequence Acoustic Modeling for Voice Conversion
- 3. ONE-SHOT VOICE CONVERSION USING STAR-GAN
- 4. SINGING VOICE CONVERSION WITH NON-PARALLEL DATA
- 5. Whether To Pretrain DNN or Not?: An Empirical Analysis for Voice Conversion
- 6. Voice Conversion using Convolutional Neural Networks 翻译
- 7. 2018ins--Wavelet Analysis of Speaker Dependent and Independent Prosody for Voice Conversion
- 8. 2016 ICME:Phonetic posteriorgrams for many-to-one voice conversion without parallel data training
- 9. Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition论文翻译
- 10. StarGAN-VC： non-parallel many-to-many voice conversion with StaGAN