Built on

W. H. Day and H. Edelsbrunner, “Efficient algorithms for agglomerative hierarchical clustering methods,” Journal of classification , vol. 1, no. 1, pp. 7–24, 1984
1984
Earlier work this paper cites.
J. J. Godfrey, E. C. Holliman, and J. McDaniel, “Switchboard: Telephone speech corpus for research and development,” in Acoustics, Speech, and Signal Processing, IEEE International Conference on , vol. 1. IEEE Computer Society, 1992, pp. 517–520
1992
Earlier work this paper cites.
S. Ioffe, “Probabilistic linear discriminant analysis,” in European Conference on Computer Vision . Springer, 2006, pp. 531–542
2006
Earlier work this paper cites.
F. Castaldo, D. Colibro, E. Dalmasso, P. Laface, and C. Vair, “Stream-based speaker segmentation using speaker factors and eigenvoices,” in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 2008, pp. 4133–4136
2008
Earlier work this paper cites.
N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788–798, 2010
2010
Earlier work this paper cites.
J. Prazak and J. Silovsky, “Speaker diarization using plda-based speaker clustering,” in Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems , vol. 1. IEEE, 2011, pp. 347–350
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 et al. , “The kaldi speech recognition toolkit,” in IEEE 2011 workshop on automatic speech recognition and understanding , no. CONF. IEEE Signal Processing Society, 2011
2011
Earlier work this paper cites.
S. H. Shum, N. Dehak, R. Dehak, and J. R. Glass, “Unsupervised methods for speaker diarization: An integrated and iterative approach,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 21, no. 10, pp. 2015–2028, 2013
2013
Earlier work this paper cites.
G. Le Lan, S. Meignier, D. Charlet, and P. Deléglise, “Speaker diarization with unsupervised training framework,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 5560–5564
2016
Earlier work this paper cites.

Similar

I. Loshchilov and F. Hutter, “Sgdr: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983 , 2016
Original
2016
Cited alongside, same era.
T. Fuchs and J. Keshet, “Spoken term detection automatically adjusted for a given threshold,” IEEE Journal of Selected Topics in Signal Processing , vol. 11, no. 8, pp. 1310–1317, 2017
2017
Cited alongside, same era.
Y. You, I. Gitman, and B. Ginsburg, “Large batch training of convolutional networks,” arXiv preprint arXiv:1708.03888 , 2017
Original
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 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5329–5333
2018
Cited alongside, same era.

Then

A. Zhang, Q. Wang, Z. Zhu, J. Paisley, and C. Wang, “Fully supervised speaker diarization,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6301–6305
Original
2019
Later among the works it cites.
F. Kreuk, J. Keshet, and Y. Adi, “Self-supervised contrastive learning for unsupervised phoneme segmentation,” arXiv preprint arXiv:2007.13465 , 2020
Original
2020
Later among the works it cites.
S. Horiguchi, Y. Fujita, S. Watanabe, Y. Xue, and K. Nagamatsu, “End-to-end speaker diarization for an unknown number of speakers with encoder-decoder based attractors,” arXiv preprint arXiv:2005.09921 , 2020
Original
2020
Later among the works it cites.
Y. Fujita, S. Watanabe, S. Horiguchi, Y. Xue, and K. Nagamatsu, “End-to-end neural diarization: Reformulating speaker diarization as simple multi-label classification,” arXiv preprint arXiv:2003.02966 , 2020
Original
2020
Later among the works it cites.

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2022

Self-supervised Speaker Diarization

Dissen, Yehoshua, Kreuk, Felix, Keshet, Joseph

Understand

Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization.

Inarguably, a significant part of this success is due to the demonstrated effectiveness of their speaker representations.
These, however, are heavily dependent on large amounts of annotated data and can be sensitive to new domains.
This study proposes an entirely unsupervised deep-learning model for speaker diarization.

Built on

W. H. Day and H. Edelsbrunner, “Efficient algorithms for agglomerative hierarchical clustering methods,” Journal of classification , vol. 1, no. 1, pp. 7–24, 1984
1984
Earlier work this paper cites.
J. J. Godfrey, E. C. Holliman, and J. McDaniel, “Switchboard: Telephone speech corpus for research and development,” in Acoustics, Speech, and Signal Processing, IEEE International Conference on , vol. 1. IEEE Computer Society, 1992, pp. 517–520
1992
Earlier work this paper cites.
S. Ioffe, “Probabilistic linear discriminant analysis,” in European Conference on Computer Vision . Springer, 2006, pp. 531–542
2006
Earlier work this paper cites.
F. Castaldo, D. Colibro, E. Dalmasso, P. Laface, and C. Vair, “Stream-based speaker segmentation using speaker factors and eigenvoices,” in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 2008, pp. 4133–4136
2008
Earlier work this paper cites.
N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788–798, 2010
2010
Earlier work this paper cites.
J. Prazak and J. Silovsky, “Speaker diarization using plda-based speaker clustering,” in Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems , vol. 1. IEEE, 2011, pp. 347–350
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 et al. , “The kaldi speech recognition toolkit,” in IEEE 2011 workshop on automatic speech recognition and understanding , no. CONF. IEEE Signal Processing Society, 2011
2011
Earlier work this paper cites.
S. H. Shum, N. Dehak, R. Dehak, and J. R. Glass, “Unsupervised methods for speaker diarization: An integrated and iterative approach,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 21, no. 10, pp. 2015–2028, 2013
2013
Earlier work this paper cites.
G. Le Lan, S. Meignier, D. Charlet, and P. Deléglise, “Speaker diarization with unsupervised training framework,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 5560–5564
2016
Earlier work this paper cites.

Similar

I. Loshchilov and F. Hutter, “Sgdr: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983 , 2016
Original
2016
Cited alongside, same era.
T. Fuchs and J. Keshet, “Spoken term detection automatically adjusted for a given threshold,” IEEE Journal of Selected Topics in Signal Processing , vol. 11, no. 8, pp. 1310–1317, 2017
2017
Cited alongside, same era.
Y. You, I. Gitman, and B. Ginsburg, “Large batch training of convolutional networks,” arXiv preprint arXiv:1708.03888 , 2017
Original
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 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5329–5333
2018
Cited alongside, same era.

Then

A. Zhang, Q. Wang, Z. Zhu, J. Paisley, and C. Wang, “Fully supervised speaker diarization,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6301–6305
Original
2019
Later among the works it cites.
F. Kreuk, J. Keshet, and Y. Adi, “Self-supervised contrastive learning for unsupervised phoneme segmentation,” arXiv preprint arXiv:2007.13465 , 2020
Original
2020
Later among the works it cites.
S. Horiguchi, Y. Fujita, S. Watanabe, Y. Xue, and K. Nagamatsu, “End-to-end speaker diarization for an unknown number of speakers with encoder-decoder based attractors,” arXiv preprint arXiv:2005.09921 , 2020
Original
2020
Later among the works it cites.
Y. Fujita, S. Watanabe, S. Horiguchi, Y. Xue, and K. Nagamatsu, “End-to-end neural diarization: Reformulating speaker diarization as simple multi-label classification,” arXiv preprint arXiv:2003.02966 , 2020
Original
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. L. Moreno, “Generalized end-to-end loss for speaker verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4879–4883

2018

Cited alongside, same era.

Q. Lin, R. Yin, M. Li, H. Bredin, and C. Barras, “Lstm based similarity measurement with spectral clustering for speaker diarization,” arXiv preprint arXiv:1907.10393 , 2019

Original

2019

Cited alongside, same era.

Y. Fujita, N. Kanda, S. Horiguchi, K. Nagamatsu, and S. Watanabe, “End-to-end neural speaker diarization with permutation-free objectives,” arXiv preprint arXiv:1909.05952 , 2019

Original

2019

Cited alongside, same era.

Y. Fujita, N. Kanda, S. Horiguchi, Y. Xue, K. Nagamatsu, and S. Watanabe, “End-to-end neural speaker diarization with self-attention,” in 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2019, pp. 296–303

2019

Cited alongside, same era.

J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow twins: Self-supervised learning via redundancy reduction,” arXiv preprint arXiv:2103.03230 , 2021

Original

2021

Later among the works it cites.

A. Bardes, J. Ponce, and Y. LeCun, “Vicreg: Variance-invariance-covariance regularization for self-supervised learning,” arXiv preprint arXiv:2105.04906 , 2021

Original

2021

Later among the works it cites.

N. Zeghidour, O. Teboul, and D. Grangier, “Dive: End-to-end speech diarization via iterative speaker embedding,” arXiv preprint arXiv:2105.13802 , 2021

Original

2021

Later among the works it cites.

S. Horiguchi, P. García, Y. Fujita, S. Watanabe, and K. Nagamatsu, “End-to-end speaker diarization as post-processing,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 7188–7192

2021

Later among the works it cites.