High Quality and Similarity One-Shot Voice Conversion Using End-to-End Model-Reference-Cited by-同舟云学术

High Quality and Similarity One-Shot Voice Conversion Using End-to-End Model

Published:2022-12-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence
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Author:

Du Renmingyue¹^ORCID,Yao Jixun²^ORCID

Affiliation:

1. School of Advanced Technology, Xi'an Jiaotong-liverpool University, China

2. School of Computer, Northwestern Polytechnical University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3577530.3577575

Reference22 articles.

1. Ju-chieh Chou Cheng-chieh Yeh and Hung-yi Lee. 2019. One-shot voice conversion by separating speaker and content representations with instance normalization. arXiv preprint arXiv:1904.05742(2019). Ju-chieh Chou Cheng-chieh Yeh and Hung-yi Lee. 2019. One-shot voice conversion by separating speaker and content representations with instance normalization. arXiv preprint arXiv:1904.05742(2019).

2. Brecht Desplanques , Jenthe Thienpondt , and Kris Demuynck . 2020 . Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification. arXiv preprint arXiv:2005.07143(2020). Brecht Desplanques, Jenthe Thienpondt, and Kris Demuynck. 2020. Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification. arXiv preprint arXiv:2005.07143(2020).

3. Daniel Garcia-Romero David Snyder Gregory Sell Alan McCree Daniel Povey and Sanjeev Khudanpur. 2019. x-Vector DNN Refinement with Full-Length Recordings for Speaker Recognition.. In Interspeech. 1493–1496. Daniel Garcia-Romero David Snyder Gregory Sell Alan McCree Daniel Povey and Sanjeev Khudanpur. 2019. x-Vector DNN Refinement with Full-Length Recordings for Speaker Recognition.. In Interspeech. 1493–1496.

4. ACVAE-VC: Non-Parallel Voice Conversion With Auxiliary Classifier Variational Autoencoder

5. CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks

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