Built on

Q. Li, F. L. Kreyssig, C. Zhang, and P. C. Woodland, “Discriminative neural clustering for speaker diarisation,” 2020, arXiv:1910.09703
Original
1910
Earlier work this paper cites.
M. Pal, M. Kumar, R. Peri, T. J. Park, S. H. Kim, C. Lord, S. Bishop, and S. Narayanan, “Speaker diarization using latent space clustering in generative adversarial network,” 2019, arXiv:1910.11398
Original
1910
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,” 2019, arXiv:1910.12592
Original
1910
Earlier work this paper cites.
J. Carletta, S. Ashby, S. Bourban, M. Flynn, M. Guillemot, T. Hain, J. Kadlec, V. Karaiskos, W. Kraaij, M. Kronenthal, G. Lathoud, M. Lincoln, A. Lisowska, I. McCowan, W. Post, D. Reidsma, and P. Wellner, “The AMI meeting corpus: A pre-announcement,” in Proc. the Second International Conference on Machine Learning for Multimodal Interaction , ser. MLMI’05, 28–39, 2006
2006
Earlier work this paper cites.
S. Ioffe, “Probabilistic linear discriminant analysis,” in ECCV , 2006, pp. 531–542
2006
Earlier work this paper cites.
M. Pal, M. Kumar, R. Peri, T. J. Park, S. H. Kim, C. Lord, S. Bishop, and S. Narayanan, “Meta-learning with latent space clustering in generative adversarial network for speaker diarization,” 2020, arXiv:2007.09635
Original
2007
Earlier work this paper cites.
U. von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing , vol. 17, no. 4, 2007
2007
Earlier work this paper cites.
X. Anguera, C. Wooters, and J. Hernando, “Acoustic beamforming for speaker diarization of meetings,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 15, no. 7, pp. 2011–2022, 2007
2007
Earlier work this paper cites.
X. Anguera, S. Bozonnet, N. Evans, C. Fredouille, G. Friedland, and O. Vinyals, “Speaker diarization: A review of recent research,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 20, no. 2, pp. 356–370, 2012
2012
Earlier work this paper cites.
F. Landini, J. Profant, M. Diez, and L. Burget, “Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks,” 2020, arXiv:2012.14952
Original
2012
Earlier work this paper cites.
A. Nagrani, J. S. Chung, J. Huh, A. Brown, E. Coto, W. Xie, M. McLaren, D. A. Reynolds, and A. Zisserman, “Voxsrc 2020: The second voxceleb speaker recognition challenge,” 2020, arXiv:2012.06867
Original
2012
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Proc. ICLR , 2014
2014
Earlier work this paper cites.
D. Snyder, G. Chen, and D. Povey, “MUSAN: A Music, Speech, and Noise Corpus,” 2015, arXiv:1510.08484v1
Original
2015
Earlier work this paper cites.

Similar

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: A large-scale speaker identification dataset,” in Proc. Interspeech , 2017, pp. 2616–2620
2017
Cited alongside, same era.
L. N. Smith, “Cyclical learning rates for training neural networks,” in IEEE WACV , 2017, pp. 464–472
2017
Cited alongside, same era.
G. Sell, D. Snyder, A. McCree, D. Garcia-Romero, J. Villalba, M. Maciejewski, V. Manohar, N. Dehak, D. Povey, S. Watanabe, and S. Khudanpur, “Diarization is hard: Some experiences and lessons learned for the JHU team in the inaugural DIHARD challenge,” in Proc. Interspeech , 2808–2812, 2018
2018
Cited alongside, same era.

Then

D. S. Park, W. Chan, Y. Zhang, C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” in Proc. Interspeech , 2019, pp. 2613–2617
2019
Later among the works it cites.
E. Hoffer, T. Ben-Nun, I. Hubara, N. Giladi, T. Hoefler, and D. Soudry, “Augment your batch: better training with larger batches,” CoRR , vol. abs/1901.09335, 2019
Original
2019
Later among the works it cites.
M. Diez, L. Burget, S. Wang, J. Rohdin, and J. Černocký, “Bayesian HMM based x-vector clustering for speaker diarization,” in Proc. Interspeech , 2019, pp. 346–350
2019
Later among the works it cites.
T. J. Park, K. J. Han, M. Kumar, and S. Narayanan, “Auto-tuning spectral clustering for speaker diarization using normalized maximum eigengap,” IEEE Signal Processing Letters , vol. 27, pp. 381–385, 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…

Reading the bibliography…

2021

ECAPA-TDNN Embeddings for Speaker Diarization

Dawalatabad, Nauman, Ravanelli, Mirco, Grondin, François et al.

Understand

Learning robust speaker embeddings is a crucial step in speaker diarization.

Deep neural networks can accurately capture speaker discriminative characteristics and popular deep embeddings such as x-vectors are nowadays a fundamental component of modern diarization systems.
Recently, some improvements over the standard TDNN architecture used for x-vectors have been proposed.
The ECAPA-TDNN model, for instance, has shown impressive performance in the speaker verification domain, thanks to a carefully designed neural model.

Built on

Q. Li, F. L. Kreyssig, C. Zhang, and P. C. Woodland, “Discriminative neural clustering for speaker diarisation,” 2020, arXiv:1910.09703
Original
1910
Earlier work this paper cites.
M. Pal, M. Kumar, R. Peri, T. J. Park, S. H. Kim, C. Lord, S. Bishop, and S. Narayanan, “Speaker diarization using latent space clustering in generative adversarial network,” 2019, arXiv:1910.11398
Original
1910
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,” 2019, arXiv:1910.12592
Original
1910
Earlier work this paper cites.
J. Carletta, S. Ashby, S. Bourban, M. Flynn, M. Guillemot, T. Hain, J. Kadlec, V. Karaiskos, W. Kraaij, M. Kronenthal, G. Lathoud, M. Lincoln, A. Lisowska, I. McCowan, W. Post, D. Reidsma, and P. Wellner, “The AMI meeting corpus: A pre-announcement,” in Proc. the Second International Conference on Machine Learning for Multimodal Interaction , ser. MLMI’05, 28–39, 2006
2006
Earlier work this paper cites.
S. Ioffe, “Probabilistic linear discriminant analysis,” in ECCV , 2006, pp. 531–542
2006
Earlier work this paper cites.
M. Pal, M. Kumar, R. Peri, T. J. Park, S. H. Kim, C. Lord, S. Bishop, and S. Narayanan, “Meta-learning with latent space clustering in generative adversarial network for speaker diarization,” 2020, arXiv:2007.09635
Original
2007
Earlier work this paper cites.
U. von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing , vol. 17, no. 4, 2007
2007
Earlier work this paper cites.
X. Anguera, C. Wooters, and J. Hernando, “Acoustic beamforming for speaker diarization of meetings,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 15, no. 7, pp. 2011–2022, 2007
2007
Earlier work this paper cites.
X. Anguera, S. Bozonnet, N. Evans, C. Fredouille, G. Friedland, and O. Vinyals, “Speaker diarization: A review of recent research,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 20, no. 2, pp. 356–370, 2012
2012
Earlier work this paper cites.
F. Landini, J. Profant, M. Diez, and L. Burget, “Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks,” 2020, arXiv:2012.14952
Original
2012
Earlier work this paper cites.
A. Nagrani, J. S. Chung, J. Huh, A. Brown, E. Coto, W. Xie, M. McLaren, D. A. Reynolds, and A. Zisserman, “Voxsrc 2020: The second voxceleb speaker recognition challenge,” 2020, arXiv:2012.06867
Original
2012
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Proc. ICLR , 2014
2014
Earlier work this paper cites.
D. Snyder, G. Chen, and D. Povey, “MUSAN: A Music, Speech, and Noise Corpus,” 2015, arXiv:1510.08484v1
Original
2015
Earlier work this paper cites.

Similar

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Cited alongside, same era.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: A large-scale speaker identification dataset,” in Proc. Interspeech , 2017, pp. 2616–2620
2017
Cited alongside, same era.
L. N. Smith, “Cyclical learning rates for training neural networks,” in IEEE WACV , 2017, pp. 464–472
2017
Cited alongside, same era.
G. Sell, D. Snyder, A. McCree, D. Garcia-Romero, J. Villalba, M. Maciejewski, V. Manohar, N. Dehak, D. Povey, S. Watanabe, and S. Khudanpur, “Diarization is hard: Some experiences and lessons learned for the JHU team in the inaugural DIHARD challenge,” in Proc. Interspeech , 2808–2812, 2018
2018
Cited alongside, same era.

Then

D. S. Park, W. Chan, Y. Zhang, C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” in Proc. Interspeech , 2019, pp. 2613–2617
2019
Later among the works it cites.
E. Hoffer, T. Ben-Nun, I. Hubara, N. Giladi, T. Hoefler, and D. Soudry, “Augment your batch: better training with larger batches,” CoRR , vol. abs/1901.09335, 2019
Original
2019
Later among the works it cites.
M. Diez, L. Burget, S. Wang, J. Rohdin, and J. Černocký, “Bayesian HMM based x-vector clustering for speaker diarization,” in Proc. Interspeech , 2019, pp. 346–350
2019
Later among the works it cites.
T. J. Park, K. J. Han, M. Kumar, and S. Narayanan, “Auto-tuning spectral clustering for speaker diarization using normalized maximum eigengap,” IEEE Signal Processing Letters , vol. 27, pp. 381–385, 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…

L. Wan, Q. Wang, A. Papir, and I. Lopez-Moreno, “Generalized end-to-end loss for speaker verification,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 4879–4883, 2018

2018

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 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 5329–5333, 2018

2018

Cited alongside, same era.

J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation networks,” in IEEE/CVF CVPR , 2018, pp. 7132–7141

2018

Cited alongside, same era.

J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Proc. Interspeech , 2018, pp. 1086–1090

2018

Cited alongside, same era.

G. Sun, C. Zhang, and P. C. Woodland, “Speaker diarisation using 2d self-attentive combination of embeddings,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 5801–5805, 2019

2019

Cited alongside, same era.

S. Gao, M.-M. Cheng, K. Zhao, X. Zhang, M.-H. Yang, and P. H. S. Torr, “Res2Net: A new multi-scale backbone architecture,” IEEE TPAMI , pp. 652–662, 2019

2019

Cited alongside, same era.

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 Proc. Interspeech , 2019, pp. 361–365

2019

Cited alongside, same era.

J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” in IEEE/CVF CVPR , 2019, pp. 4685–4694

2019

Cited alongside, same era.

B. Desplanques, J. Thienpondt, and K. Demuynck, “ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification,” in Proc. Interspeech , 2020, pp. 3830–3834

2020

Later among the works it cites.

H. Zeinali, K. A. Lee, J. Alam, and L. Burget, “SdSV challenge 2020: Large-scale evaluation of short-duration speaker verification,” in Proc. Interspeech , 2020, pp. 731–735

2020

Later among the works it cites.

J. Thienpondt, B. Desplanques, and K. Demuynck, “Cross-lingual speaker verification with domain-balanced hard prototype mining and language-dependent score normalization,” in Proc. Interspeech , 2020, pp. 756–760

2020

Later among the works it cites.

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

2020

Later among the works it cites.

T. Park, N. Kanda, D. Dimitriadis, K. J. Han, S. Watanabe, and S. S. Narayanan, “A review of speaker diarization: Recent advances with deep learning,” ArXiv , vol. abs/2101.09624, 2021

Original

2021

Closest in time.

N. Dawalatabad, S. Madikeri, C. C. Sekhar, and H. A. Murthy, “Novel architectures for unsupervised information bottleneck based speaker diarization of meetings,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 14–27, 2021

2021

Closest in time.

M. Ravanelli, T. Parcollet, A. Rouhe, P. Plantinga, E. Rastorgueva, L. Lugosch, N. Dawalatabad, C. Ju-Chieh, A. Heba, F. Grondin, W. Aris, C.-F. Liao, S. Cornell, S.-L. Yeh, H. Na, Y. Gao, S.-W. Fu, C. Subakan, R. De Mori, and Y. Bengio, “Speechbrain,” https://github.com/speechbrain/speechbrain

2021

Closest in time.

Xavier Anguera, “Diarization Error Rate,” http://www.xavieranguera.com/phdthesis/node108.html

2021

Closest in time.