Fetching the paper…
Reading the bibliography…
Training robust speaker verification systems without speaker labels has long been a challenging task.
“Voxceleb: A large-scale speaker identification dataset,”
Arsha Nagrani, Joon Son Chung, and Andrew Zisserman, · 2017
Earlier work this paper cites.
“Voxceleb2: Deep speaker recognition,”
Joon Son Chung, Arsha Nagrani, and Andrew Zisserman, · 2018
Earlier work this paper cites.
“Representation learning with contrastive predictive coding,”
Aäron van den Oord, Yazhe Li, and Oriol Vinyals, · 2018
Earlier work this paper cites.
“X-vectors: Robust DNN embeddings for speaker recognition,”
David Snyder, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur, · 2018
Earlier work this paper cites.
“Autoencoder-based semi-supervised curriculum learning for out-of-domain speaker verification,”
Siqi Zheng, Gang Liu, Hongbin Suo, and Yun Lei, · 2019
Earlier work this paper cites.
“Towards a fault-tolerant speaker verification system: A regularization approach to reduce the condition number,”
Siqi Zheng, Gang Liu, Hongbin Suo, and Yun Lei, · 2019
Earlier work this paper cites.
“Specaugment: A simple data augmentation method for automatic speech recognition,”
Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le, · 2019
Earlier work this paper cites.
“A simple framework for contrastive learning of visual representations,”
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton, · 2020
Earlier work this paper cites.
“Momentum contrast for unsupervised visual representation learning,”
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick, · 2020
Cited alongside, same era.
“Bootstrap your own latent - A new approach to self-supervised learning,”
Jean-Bastien Grill, Florian Strub, Florent Altché, and et al., · 2020
Cited alongside, same era.
“Augmentation adversarial training for unsupervised speaker recognition,”
Jaesung Huh, Hee Soo Heo, Jingu Kang, Shinji Watanabe, and Joon Son Chung, · 2020
Cited alongside, same era.
“Unsupervised representation learning for speaker recognition via contrastive equilibrium learning,”
Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han, and Nam Soo Kim, · 2020
Cited alongside, same era.
“Self-supervised text-independent speaker verification using prototypical momentum contrastive learning,”
Wei Xia, Chunlei Zhang, Chao Weng, Meng Yu, and Dong Yu, · 2021
Cited alongside, same era.
“Self-supervised speaker recognition with loss-gated learning,”
Ruijie Tao, Kong Aik Lee, Rohan Kumar Das, Ville Hautamäki, and Haizhou Li, · 2022
Closest in time.
“Pushing the limits of raw waveform speaker recognition,”
Jee-weon Jung, You Jin Kim, Hee-Soo Heo, Bong-Jin Lee, Youngki Kwon, and Joon Son Chung, · 2022
Closest in time.
“Self-supervised curriculum learning for speaker verification,”
Hee-Soo Heo, Jee-weon Jung, Jingu Kang, Youngki Kwon, You Jin Kim, and Bong-Jin Lee abd Joon Son Chung, · 2022
Closest in time.
“Self-supervised speaker verification with simple siamese network and self-supervised regularization,”
Mufan Sang, Haoqi Li, Fang Liu, Andrew O. Arnold, and Li Wan, · 2022
Closest in time.
“Self-supervised speaker verification using dynamic loss-gate and label correction,”
Bing Han, Zhengyang Chen, and Yanmin Qian, · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“An iterative framework for self-supervised deep speaker representation learning,”
Danwei Cai, Weiqing Wang, and Ming Li, · 2021
Cited alongside, same era.
“The jhu submission to voxsrc-21: Track 3,”
Jejin Cho, Jesus Villalba, and Najim Dehak, · 2021
Cited alongside, same era.
“Emerging properties in self-supervised vision transformers,”
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin, · 2021
Cited alongside, same era.
“PRISM: pre-trained indeterminate speaker representation model for speaker diarization and speaker verification,”
Siqi Zheng, Hongbin Suo, and Qian Chen, · 2022
Closest in time.
“Vicreg: Variance-invariance-covariance regularization for self-supervised learning,”
Adrien Bardes, Jean Ponce, and Yann LeCun, · 2022
Closest in time.
“Acoustic feature shuffling network for text-independent speaker verification,”
Jin Li, Xin Fang, Fan Chu, Tian Gao, Yan Song, and Rong Li Dai, · 2022
Closest in time.