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Domain generalization remains a critical problem for speaker recognition, even with the state-of-the-art architectures based on deep neural nets.
S. Bengio, Y. Bengio, J. Cloutier, and J. Gecsei, “On the optimization of a synaptic learning rule,” in
1992
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,”
2002
Earlier work this paper cites.
S. Ioffe, “Probabilistic linear discriminant analysis,” in
2006
Earlier work this paper cites.
N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,”
2011
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
J. Villalba and E. Lleida, “Bayesian adaptation of PLDA based speaker recognition to domains with scarce development data,” in
2012
Earlier work this paper cites.
E. Variani, X. Lei, E. McDermott, I. L. Moreno, and J. Gonzalez-Dominguez, “Deep neural networks for small footprint text-dependent speaker verification,” in
2014
Earlier work this paper cites.
D. Garcia-Romero and A. McCree, “Supervised domain adaptation for i-vector based speaker recognition,” in
2014
Earlier work this paper cites.
D. Garcia-Romero, A. McCree, S. Shum, and C. Vaquero, “Unsupervised domain adaptation for i-vector speaker recognition,” in
2014
Earlier work this paper cites.
J. H. Hansen and T. Hasan, “Speaker recognition by machines and humans: A tutorial review,”
2015
Earlier work this paper cites.
D. Wang and T. F. Zheng, “Transfer learning for speech and language processing,” in
2015
Earlier work this paper cites.
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas, “Learning to learn by gradient descent by gradient descent,” in
2016
Earlier work this paper cites.
M. McLaren, L. Ferrer, D. Castan, and A. Lawson, “The speakers in the wild (SITW) speaker recognition database.” in
2016
Earlier work this paper cites.
L. Li, Y. Chen, Y. Shi, Z. Tang, and D. Wang, “Deep speaker feature learning for text-independent speaker verification,” in
2017
Cited alongside, same era.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in
2017
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “VoxCeleb: a large-scale speaker identification dataset,” in
2017
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.
K. Okabe, T. Koshinaka, and K. Shinoda, “Attentive statistics pooling for deep speaker embedding,” in
2018
Cited alongside, same era.
J. Wang, K.-C. Wang, M. T. Law, F. Rudzicz, and M. Brudno1, “Centroid-based deep metric learning for speaker recognition,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
Z. Gao, Y. Song, I. McLoughlin, P. Li, Y. Jiang, and L.-R. Dai, “Improving aggregation and loss function for better embedding learning in end-to-end speaker verification system,” in
2019
Later among the works it cites.
J. Zhou, T. Jiang, Z. Li, L. Li, and Q. Hong, “Deep speaker embedding extraction with channel-wise feature responses and additive supervision softmax loss function,” in
2019
Later among the works it cites.
R. Li, N. L. D. Tuo, M. Yu, D. Su, and D. Yu, “Boundary discriminative large margin cosine loss for text-independent speaker verification,” in
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2018
Cited alongside, same era.
W. Cai, J. Chen, and M. Li, “Exploring the encoding layer and loss function in end-to-end speaker and language recognition system,” in
2018
Cited alongside, same era.
W. Ding and L. He, “MTGAN: Speaker verification through multitasking triplet generative adversarial networks,” in
2018
Cited alongside, same era.
Q. Wang, W. Rao, S. Sun, L. Xie, E. S. Chng, and H. Li, “Unsupervised domain adaptation via domain adversarial training for speaker recognition,” in
2018
Cited alongside, same era.
J. Vanschoren, “Meta-learning: A survey,”
2018
Cited alongside, same era.
J. weon Jung, H.-S. Heo, J. ho Kim, H. jin Shim, and H.-J. Yu, “RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification,” in
2019
Cited alongside, same era.
W. Xie, A. Nagrani, J. S. Chung, and A. Zisserman, “Utterance-level aggregation for speaker recognition in the wild,” in
2019
Cited alongside, same era.
2019
Later among the works it cites.
S. Wang, J. Rohdin, L. Burget, O. Plchot, Y. Qian, K. Yu, and J. Cernocky, “On the usage of phonetic information for text-independent speaker embedding extraction,” in
2019
Later among the works it cites.
T. Stafylakis, J. Rohdin, O. Plchot, P. Mizera, and L. Burget, “Self-supervised speaker embeddings,” in
2019
Later among the works it cites.
S. O. Sadjadi, C. Greenberg, E. Singer, D. Reynolds, L. Mason, and J. Hernandez-Cordero, “The 2018 NIST speaker recognition evaluation,” in
2019
Later among the works it cites.
Q. Qian, S. Zhu, J. Tang, R. Jin, B. Sun, and H. Li, “Robust optimization over multiple domains,” in
2019
Later among the works it cites.
Q. Dou, D. C. de Castro, K. Kamnitsas, and B. Glocker, “Domain generalization via model-agnostic learning of semantic features,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in
2019
Later among the works it cites.
Y. Fan, J. Kang, L. Li, K. Li, H. Chen, S. Cheng, P. Zhang, Z. Zhou, Y. Cai, and D. Wang, “CN-CELEB: a challenging chinese speaker recognition dataset,” in
2020
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