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This paper focuses on using voice conversion (VC) to improve the speech intelligibility of surgical patients who have had parts of their articulators removed.
Y. Stylianou, O. Cappé, and E. Moulines, “Continuous probabilistic transform for voice conversion,”
1998
Earlier work this paper cites.
A. Kain and M. W. Macon, “Spectral voice conversion for text-to-speech synthesis,” in
1998
Earlier work this paper cites.
D. Rentzos, S. Vaseghi, E. Turajlic, Q. Yan, and C.-H. Ho, “Transformation of speaker characteristics for voice conversion,” in
2003
Earlier work this paper cites.
H. Kawanami, Y. Iwami, T. Toda, H. Saruwatari, and K. Shikano, “GMM-based voice conversion applied to emotional speech synthesis,” in
2003
Earlier work this paper cites.
A. B. Kain, J. P. Hosom, X. Niu, J. P. van Santen, M. Fried-Oken, and J. Staehely, “Improving the intelligibility of dysarthric speech,”
2007
Earlier work this paper cites.
D. Saito, K. Yamamoto, N. Minematsu, and K. Hirose, “One-to-many voice conversion based on tensor representation of speaker space,” in
2011
Earlier work this paper cites.
F. Rudzicz, “Acoustic transformations to improve the intelligibility of dysarthric speech,” in
2011
Earlier work this paper cites.
R. Aihara, R. Takashima, T. Takiguchi, and Y. Ariki, “GMM-based emotional voice conversion using spectrum and prosody features,”
2012
Earlier work this paper cites.
K. Nakamura, T. Toda, H. Saruwatari, and K. Shikano, “Speaking-aid systems using gmm-based voice conversion for electrolaryngeal speech,”
2012
Earlier work this paper cites.
R. Aihara, R. Takashima, T. Takiguchi, and Y. Ariki, “Consonant enhancement for articulation disorders based on non-negative matrix factorization,” in
2012
Earlier work this paper cites.
——, “Individuality-preserving voice conversion for articulation disorders based on non-negative matrix factorization,” in
2013
Cited alongside, same era.
C. Hsu, H. Hwang, Y. Wu, Y. Tsao, and H. Wang, “Voice conversion from non-parallel corpora using variational auto-encoder,” in
2016
Cited alongside, same era.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in
2016
Cited alongside, same era.
2016
Cited alongside, same era.
D. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (ELUs),” in
2016
Cited alongside, same era.
X. Mao, Q. Li, H. Xie, R. Lau, Z. Wang, and S. Smolley, “Least squares generative adversarial networks,” in
2017
Later among the works it cites.
P. Isola, J. Zhu, T. Zhou, and A. Efros, “Image-to-image translation with conditional adversarial networks,” in
2017
Later among the works it cites.
J. Zhu, T. Park, P. Isola, and A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in
2017
Later among the works it cites.
C. Chu, A. Zhmoginov, and M. Sandler, “Cyclegan, a master of steganography,”
2017
Later among the works it cites.
Y. Gao, R. Singh, and B. Raj, “Voice impersonation using generative adversarial networks,” in
2018
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T. Kinnunen, L. Juvela, P. Alku, and J. Yamagishi, “Non-parallel voice conversion using i-vector plda: Towards unifying speaker verification and transformation,” in
2017
Cited alongside, same era.
K. Oyamada, H. Kameoka, T. Kaneko, H. Ando, K. Hiramatsu, and K. Kashino, “Non-native speech conversion with consistency-aware recursive network and generative adversarial network,” in
2017
Cited alongside, same era.
S. Fu, P. Li, Y. Lai, C. Yang, L. Hsieh, and Y. Tsao, “Joint dictionary learning-based non-negative matrix factorization for voice conversion to improve speech intelligibility after oral surgery,”
2017
Cited alongside, same era.
——, “Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks,” in
2017
Cited alongside, same era.
J. Chou, C. Yeh, H. Lee, and L. Lee, “Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations,” in
2018
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P. Bojanowski, A. Joulin, D. Lopez-Paz, and A. Szlam, “Optimizing the latent space of generative networks,” in
2018
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2018
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E. Dunbar, R. Algayres, J. Karadayi, M. Bernard, J. Benjumea, X.-N. Cao, L. Miskic, C. Dugrain, L. Ondel, A. W. Black, L. Besacier, S. Sakti, and E. Dupoux, “The Zero Resource Speech Challenge 2019: TTS without T,” in
2019
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