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The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset.
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2017
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2017
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2019
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2019
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J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in
2017
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J. Lorenzo-Trueba, J. Yamagishi, T. Toda, D. Saito, F. Villavicencio, T. Kinnunen, and Z. Ling, “The voice conversion challenge 2018: Promoting development of parallel and nonparallel methods,” in
2018
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X. Tian, J. Wang, H. Xu, E. S. Chng, and H. Li, “Average modeling approach to voice conversion with non-parallel data.” in
2018
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L.-J. Liu, Z.-H. Ling, Y. Jiang, M. Zhou, and L.-R. Dai, “WaveNet vocoder with limited training data for voice conversion,” in
2018
Cited alongside, same era.
D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in
2018
Cited alongside, same era.
T. Kaneko and H. Kameoka, “Cyclegan-vc: Non-parallel voice conversion using cycle-consistent adversarial networks,” in
2018
Cited alongside, same era.
H. Kameoka, T. Kaneko, K. Tanaka, and N. Hojo, “Stargan-vc: Non-parallel many-to-many voice conversion using star generative adversarial networks,” in
2018
Cited alongside, same era.
2019
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M. Patel, M. Purohit, M. Parmar, N. J. Shah, and H. A. Patil, “Adagan: Adaptive gan for many-to-many non-parallel voice conversion,” 2019
2019
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J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in
2019
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R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in
2019
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2019
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2019
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2020
Closest in time.
T. Hayashi, R. Yamamoto, K. Inoue, T. Yoshimura, S. Watanabe, T. Toda, K. Takeda, Y. Zhang, and X. Tan, “Espnet-tts: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit,” in
2020
Closest in time.
2020
Closest in time.
H.-T. Luong and J. Yamagishi, “NAUTILUS: a versatile voice cloning system,”
2020
Closest in time.
R. Yamamoto, E. Song, and J.-M. Kim, “Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram,” in
2020
Closest in time.
T. Kinnunen, R. K. Das, Z. Ling, Y. Zhao, J. Yamagishi, X. Tian, W.-C. Huang, and T. Toda, “Objective assessment of voice conversion challenge 2020 submissions,” in
2020
Closest in time.