Fetching the paper…
Reading the bibliography…
Voice conversion (VC) is a task that transforms the source speaker's timbre, accent, and tones in audio into another one's while preserving the linguistic content.
Y. Stylianou, O. Cappé, and E. Moulines, “Continuous probabilistic transform for voice conversion,”
1998
Earlier work this paper cites.
T. Toda, A. W. Black, and K. Tokuda, “Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory,”
2007
Earlier work this paper cites.
E. Helander, T. Virtanen, J. Nurminen, and M. Gabbouj, “Voice conversion using partial least squares regression,”
2010
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
Earlier work this paper cites.
L. Sun, K. Li, H. Wang, S. Kang, and H. Meng, “Phonetic posteriorgrams for many-to-one voice conversion without parallel data training,”
2016
Earlier work this paper cites.
C.-C. Hsu, H.-T. Hwang, Y.-C. Wu, Y. Tsao, and H.-M. Wang, “Voice conversion from non-parallel corpora using variational auto-encoder,”
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Veaux, J. Yamagishi, K. MacDonald
2016
Earlier work this paper cites.
X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in
2017
Earlier work this paper cites.
A. van den Oord, O. Vinyals
2017
Cited alongside, same era.
2017
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.
S. Liu, J. Zhong, L. Sun, X. Wu, X. Liu, and H. Meng, “Voice conversion across arbitrary speakers based on a single target-speaker utterance,”
2018
Cited alongside, same era.
K. Qian, Y. Zhang, S. Chang, X. Yang, and M. Hasegawa-Johnson, “Autovc: Zero-shot voice style transfer with only autoencoder loss,” 2019
2019
Later among the works it cites.
J. chieh Chou, C. chieh Yeh, and H. yi Lee, “One-shot voice conversion by separating speaker and content representations with instance normalization,”
2019
Later among the works it cites.
S. Vasquez and M. Lewis, “Melnet: A generative model for audio in the frequency domain,”
2019
Later among the works it cites.
J.-Y. Liu, Y.-H. Chen, Y.-C. Yeh, and Y.-H. Yang, “Score and lyrics-free singing voice generation,”
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
J. Serrà, S. Pascual, and C. S. Perales, “Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion,” in
2019
Cited alongside, same era.
H. Lu, Z. Wu, D. Dai, R. Li, S. Kang, J. Jia, and H. Meng, “One-shot voice conversion with global speaker embeddings,”
2019
Cited alongside, same era.
A. T. Liu, P. chun Hsu, and H. yi Lee, “Unsupervised end-to-end learning of discrete linguistic units for voice conversion,”
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
K. Kumar, R. Kumar, T. de Boissiere, L. Gestin, W. Zhen, T. J. Sotelo, A. de Brébisson, Y. Bengio, and A. C. Courville, “Melgan: Generative adversarial networks for conditional waveform synthesis,”
2019
Later among the works it cites.
K. Qian, Z. Jin, M. Hasegawa-Johnson, and G. J. Mysore, “F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder,” in
2020
Closest in time.
D.-Y. Wu and H. yi Lee, “One-shot voice conversion by vector quantization,”
2020
Closest in time.