Date: 202207
Problem Definition : Voice Conversion Deep Learning Modeling
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Datasets
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Methodology
Overview
- Autoencoder
β’ Ada-VC:
Chou, Ju-chieh, Cheng-chieh Yeh, and Hung-yi Lee. "One-shot voice conversion by separating speaker and content representations with instance normalization." arXiv preprint arXiv:1904.05742 (2019).
β’ FragmentVC:
Lin, Yist Y., et al. "Fragmentvc: Any-to-any voice conversion by end-to-end extracting and fusing fine-grained voice fragments with attention." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.
- StarGAN
β’ StarGAN-VC:
Kameoka, Hirokazu, et al. "Stargan-vc: Non-parallel many-to-many voice conversion using star generative adversarial networks." 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018.
β’ StarGAN-VC-2
Kaneko, Takuhiro, et al. "StarGAN-VC2: Rethinking conditional methods for StarGAN-based voice conversion." arXiv preprint arXiv:1907.12279 (2019).
Autoencoder β Ada-VC
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StarGAN β VC
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StarGAN β VC2
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Cold down mechanism
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Evaluation metrics
MOS, speaker embedding cosine similarity
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Mean Opinion Score
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Speaker embedding cosine similarity
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Experiment Setup
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Result
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