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Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed.
“Speaker verification using adapted gaussian mixture models,”
D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, · 2000
Earlier work this paper cites.
“Discriminatively trained probabilistic linear discriminant analysis for speaker verification,”
L. Burget, O. Plchot, S. Cumani, O. Glembek, P. Matějka, and N. Brümmer, · 2011
Earlier work this paper cites.
“Front-end factor analysis for speaker verification,”
N. Dehak, P. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, · 2011
Earlier work this paper cites.
“Promoting robustness for speaker modeling in the community: the prism evaluation set,”
L. Ferrer, H. Bratt, L. Burget, H. Cernocky, O. Glembek, M. Graciarena, A. Lawson, Y. Lei, P. Matejka, O. Plchot, et al., · 2012
Earlier work this paper cites.
“Pairwise discriminative speaker verification in the i–vector space,”
S. Cumani, N. Brümmer, L. Burget, P. Laface, O. Plchot, and V. Vasilakis, · 2013
Earlier work this paper cites.
“A novel scheme for speaker recognition using a phonetically-aware deep neural network,”
Y. Lei, N. Scheffer, L. Ferrer, and M. McLaren, · 2014
Earlier work this paper cites.
“Deep belief networks for i-vector based speaker recognition,”
O. Ghahabi and J. Hernando, · 2014
Earlier work this paper cites.
“Deep neural networks for small footprint text-dependent speaker verification,”
E. Variani, X. Lei, E. McDermott, I. L. Moreno, and J. Gonzalez-Dominguez, · 2014
Earlier work this paper cites.
“But 2014 babel system: Analysis of adaptation in nn based systems,”
M. Karafiát, F. Grézl, K. Veselý, M. Hannemann, I. Szőke, and J. Černocký, · 2014
Cited alongside, same era.
“Adam: A method for stochastic optimization,”
D. P. Kingma and J. Ba, · 2014
Cited alongside, same era.
“Non-linear plda for i-vector speaker verification,”
S. Novoselov, T. Pekhovsky, O. Kudashev, V. S. Mendelev, and A. Prudnikov, · 2015
Cited alongside, same era.
“Analysis and optimization of bottleneck features for speaker recognition,”
A. Lozano-Diez, A. Silnova, P. Matějka, O. Glembek, O. Plchot, J. Pešán, L. Burget, and J. Gonzalez-Rodriguez, · 2016
Cited alongside, same era.
“Modelling speaker and channel variability using deep neural networks for robust speaker verification,”
G. Bhattacharya, J. Alam, P. Kenny, and V. Gupta, · 2016
Cited alongside, same era.
“The 2016 NIST speaker recognition evaluation plan (sre16),” https://www.nist.gov/file/325336
2016
Later among the works it cites.
“Theano: A Python framework for fast computation of mathematical expressions,”
Theano Development Team, · 2016
Later among the works it cites.
“Robust discriminative training against data insufficiency in plda-based speaker verification,”
J. Rohdin, S. Biswas, and K. Shinoda, · 2016
Later among the works it cites.
“Deep speaker embeddings for short-duration speaker verification,”
G. Bhattacharya, J. Alam, and P. Kenny, · 2017
Closest in time.
“Deep neural network embeddings for text-independent speaker verification,”
D. Snyder, D. Garcia-Romero, D. Povey, and S. Khudanpur, · 2017
Closest in time.
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“End-to-end text-dependent speaker verification,”
G. Heigold, I. Moreno, S. Bengio, and N. Shazeer, · 2016
Cited alongside, same era.
“End-to-end attention based text-dependent speaker verification,”
S. X. Zhang, Z. Chen, Y. Zhao, J. Li, and Y. Gong, · 2016
Cited alongside, same era.
“Deep neural network-based speaker embeddings for end-to-end speaker verification,”
D. Snyder, P. Ghahremani, D. Povey, D. Garcia-Romero, Y. Carmiel, and S. Khudanpur, · 2016
Cited alongside, same era.
O. Plchot, P. Matějka, A. Silnova, O. Novotný, M. Diez, J. Rohdin, O. Glembek, N. Brümmer, A. Swart, J. Jorrín-Prieto, P. García, L. Buera, P. Kenny, J. Alam, and G. Bhattacharya, · 2017
Closest in time.
“End-to-end DNN Based Speaker Recognition Inspired by i-vector and PLDA,”
J. Rohdin, A. Silnova, M. Diez, O. Plchot, P. Matejka, and L. Burget, · 2017
Closest in time.