Built on

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR , vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385
Original
2015
Earlier work this paper cites.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” arXiv preprint arXiv:1706.08612 , 2017
Original
2017
Earlier work this paper cites.
D. Colibro, C. Vair, E. Dalmasso, K. Farrell, G. Karvitsky, S. Cumani, and P. Laface, “Nuance–Politecnico di Torino’s 2016 NIST speaker recognition evaluation system,” in INTERSPEECH 2017 , Stockholm, Sweden, August 2017, pp. 1338–1342
2017
Earlier work this paper cites.
S. O. Sadjadi, T. Kheyrkhah, A. Tong, C. S. Greenberg, D. A. Reynolds, E. Singer, L. P. Mason, J. Hernandez-Cordero et al. , “The 2016 nist speaker recognition evaluation.” in Interspeech , 2017, pp. 1353–1357
2017
Earlier work this paper cites.
A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv preprint arXiv:1807.03748 , 2018
Original
2018
Earlier work this paper cites.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” arXiv preprint arXiv:1806.05622 , 2018
Original
2018
Earlier work this paper cites.
L. N. Smith and N. Topin, “Super-convergence: Very fast training of neural networks using large learning rates,” 2018
2018
Earlier work this paper cites.
S. Novoselov, A. Shulipa, I. Kremnev, A. Kozlov, and V. Shchemelinin, “On deep speaker embeddings for text-independent speaker recognition,” arXiv preprint arXiv:1804.10080 , 2018
Original
2018
Earlier work this paper cites.
“Nist 2018 speaker recognition evaluation plan,” Available: https://www.nist.gov/system/files/documents /2018/08/17/sre18 eval plan 2018-05-31 v6.pdf, 2018
2018
Earlier work this paper cites.
C. Cieri, M. Liberman, S. Strassel, D. DiPersio, J. Wright, and A. Mazzucchi, “From ‘solved problems’ to new challenges: A report on ldc activities,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , 2018
2018
Earlier work this paper cites.
H. Zeinali, S. Wang, A. Silnova, P. Matějka, and O. Plchot, “BUT System Description to VoxCeleb Speaker Recognition Challenge 2019,” Tech. Rep., 2019. [Online]. Available: http://www.openslr.org/17/
2019
Earlier work this paper cites.

Similar

K. A. Lee, H. Yamamoto, K. Okabe, Q. Wang, L. Guo, T. Koshinaka, J. Zhang, and K. Shinoda, “The nec-tt 2018 speaker verification system.” in INTERSPEECH , 2019, pp. 4355–4359
2019
Cited alongside, same era.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” arXiv preprint arXiv:1904.05862 , 2019
Original
2019
Cited alongside, same era.
M. Ott, S. Edunov, A. Baevski, A. Fan, S. Gross, N. Ng, D. Grangier, and M. Auli, “fairseq: A fast, extensible toolkit for sequence modeling,” arXiv preprint arXiv:1904.01038 , 2019
Original
2019
Cited alongside, same era.
D. Garcia-Romero, D. Snyder, G. Sell, A. McCree, D. Povey, and S. Khudanpur, “x-vector dnn refinement with full-length recordings for speaker recognition.” in Interspeech , 2019, pp. 1493–1496
2019
Cited alongside, same era.

Then

J. Lopez, D. Garcia-Romero, N. Chen, G. Sell, B. Borgstrom, A. McCree, P. Garcia, S. Kataria, P. Nidadavolu, P. Torres-Carrasquiilo, and N. Dehak, “Advances in speaker recognition for telephone and audio-visual data: the jhu-mit submission for nist sre19,” 11 2020, pp. 273–280
2020
Later among the works it cites.
B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,” arXiv preprint arXiv:2005.07143 , 2020
Original
2020
Later among the works it cites.
S. O. Sadjadi, C. Greenberg, E. Singer, D. Reynolds, L. Mason, J. Hernandez-Cordero et al. , “The 2019 nist speaker recognition evaluation cts challenge,” in Speaker Odyssey , vol. 2020, 2020, pp. 266–272
2020
Later among the works it cites.
S. O. Sadjadi, C. S. Greenberg, E. Singer, D. A. Reynolds, L. Mason et al. , “Nist 2020 cts speaker recognition challenge evaluation plan,” 2020
2020
Later among the works it cites.

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2022

Robust Speaker Recognition with Transformers Using wav2vec 2.0

Novoselov, Sergey, Lavrentyeva, Galina, Avdeeva, Anastasia et al.

Understand

Recent advances in unsupervised speech representation learning discover new approaches and provide new state-of-the-art for diverse types of speech processing tasks.

This paper presents an investigation of using wav2vec 2.0 deep speech representations for the speaker recognition task.
The proposed fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling back-end using additive angular margin loss allows to obtain deep speaker embedding extractor that is well-generalized across different domains.
It is concluded that Contrastive Predictive Coding pretraining scheme efficiently utilizes the power of unlabeled data, and thus opens the door to powerful transformer-based speaker recognition systems.

Built on

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” CoRR , vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385
Original
2015
Earlier work this paper cites.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” arXiv preprint arXiv:1706.08612 , 2017
Original
2017
Earlier work this paper cites.
D. Colibro, C. Vair, E. Dalmasso, K. Farrell, G. Karvitsky, S. Cumani, and P. Laface, “Nuance–Politecnico di Torino’s 2016 NIST speaker recognition evaluation system,” in INTERSPEECH 2017 , Stockholm, Sweden, August 2017, pp. 1338–1342
2017
Earlier work this paper cites.
S. O. Sadjadi, T. Kheyrkhah, A. Tong, C. S. Greenberg, D. A. Reynolds, E. Singer, L. P. Mason, J. Hernandez-Cordero et al. , “The 2016 nist speaker recognition evaluation.” in Interspeech , 2017, pp. 1353–1357
2017
Earlier work this paper cites.
A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv preprint arXiv:1807.03748 , 2018
Original
2018
Earlier work this paper cites.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” arXiv preprint arXiv:1806.05622 , 2018
Original
2018
Earlier work this paper cites.
L. N. Smith and N. Topin, “Super-convergence: Very fast training of neural networks using large learning rates,” 2018
2018
Earlier work this paper cites.
S. Novoselov, A. Shulipa, I. Kremnev, A. Kozlov, and V. Shchemelinin, “On deep speaker embeddings for text-independent speaker recognition,” arXiv preprint arXiv:1804.10080 , 2018
Original
2018
Earlier work this paper cites.
“Nist 2018 speaker recognition evaluation plan,” Available: https://www.nist.gov/system/files/documents /2018/08/17/sre18 eval plan 2018-05-31 v6.pdf, 2018
2018
Earlier work this paper cites.
C. Cieri, M. Liberman, S. Strassel, D. DiPersio, J. Wright, and A. Mazzucchi, “From ‘solved problems’ to new challenges: A report on ldc activities,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) , 2018
2018
Earlier work this paper cites.
H. Zeinali, S. Wang, A. Silnova, P. Matějka, and O. Plchot, “BUT System Description to VoxCeleb Speaker Recognition Challenge 2019,” Tech. Rep., 2019. [Online]. Available: http://www.openslr.org/17/
2019
Earlier work this paper cites.

Similar

K. A. Lee, H. Yamamoto, K. Okabe, Q. Wang, L. Guo, T. Koshinaka, J. Zhang, and K. Shinoda, “The nec-tt 2018 speaker verification system.” in INTERSPEECH , 2019, pp. 4355–4359
2019
Cited alongside, same era.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” arXiv preprint arXiv:1904.05862 , 2019
Original
2019
Cited alongside, same era.
M. Ott, S. Edunov, A. Baevski, A. Fan, S. Gross, N. Ng, D. Grangier, and M. Auli, “fairseq: A fast, extensible toolkit for sequence modeling,” arXiv preprint arXiv:1904.01038 , 2019
Original
2019
Cited alongside, same era.
D. Garcia-Romero, D. Snyder, G. Sell, A. McCree, D. Povey, and S. Khudanpur, “x-vector dnn refinement with full-length recordings for speaker recognition.” in Interspeech , 2019, pp. 1493–1496
2019
Cited alongside, same era.

Then

J. Lopez, D. Garcia-Romero, N. Chen, G. Sell, B. Borgstrom, A. McCree, P. Garcia, S. Kataria, P. Nidadavolu, P. Torres-Carrasquiilo, and N. Dehak, “Advances in speaker recognition for telephone and audio-visual data: the jhu-mit submission for nist sre19,” 11 2020, pp. 273–280
2020
Later among the works it cites.
B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,” arXiv preprint arXiv:2005.07143 , 2020
Original
2020
Later among the works it cites.
S. O. Sadjadi, C. Greenberg, E. Singer, D. Reynolds, L. Mason, J. Hernandez-Cordero et al. , “The 2019 nist speaker recognition evaluation cts challenge,” in Speaker Odyssey , vol. 2020, 2020, pp. 266–272
2020
Later among the works it cites.
S. O. Sadjadi, C. S. Greenberg, E. Singer, D. A. Reynolds, L. Mason et al. , “Nist 2020 cts speaker recognition challenge evaluation plan,” 2020
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

H. Zeinali, S. Wang, A. Silnova, P. Matejka, and O. Plchot, “But system description to voxceleb speaker recognition challenge 2019,” 10 2019

2019

Cited alongside, same era.

M. K. Nandwana, J. Van Hout, M. McLaren, C. Richey, A. Lawson, and M. A. Barrios, “The voices from a distance challenge 2019 evaluation plan,” arXiv preprint arXiv:1902.10828 , 2019

Original

2019

Cited alongside, same era.

D. Garcia-Romero, G. Sell, and A. McCree, “Magneto: X-vector magnitude estimation network plus offset for improved speaker recognition,” in Proc. Odyssey 2020 the speaker and language recognition workshop , 2020, pp. 1–8

2020

Cited alongside, same era.

A. Gusev, V. Volokhov, T. Andzhukaev, S. Novoselov, G. Lavrentyeva, M. Volkova, A. Gazizullina, A. Shulipa, A. Gorlanov, A. Avdeeva et al. , “Deep speaker embeddings for far-field speaker recognition on short utterances,” arXiv preprint arXiv:2002.06033 , 2020

Original

2020

Cited alongside, same era.

A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” arXiv preprint arXiv:2006.11477 , 2020

Original

2020

Cited alongside, same era.

Z. Fan, M. Li, S. Zhou, and B. Xu, “Exploring wav2vec 2.0 on speaker verification and language identification,” arXiv preprint arXiv:2012.06185 , 2020

Original

2020

Cited alongside, same era.

O. Sadjadi, C. Greenberg, E. Singer, L. Mason, D. Reynolds et al. , “Nist 2021 speaker recognition evaluation plan,” 2021

2021