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Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) · 2006
Earlier work this paper cites.
X. Anguera, “Beamformit, the fast and robust acoustic beamformer,” 2006
2006
Earlier work this paper cites.
P. Kenny, “Bayesian analysis of speaker diarization with eigenvoice priors,” tech. rep., Montreal: CRIM, 2008
2008
Earlier work this paper cites.
T. Nakatani, T. Yoshioka, K. Kinoshita, M. Miyoshi, and B. H. Juang, “Speech dereverberation based on variance-normalized delayed linear prediction,” IEEE Transactions on Audio, Speech, and Language Processing
2010
Earlier work this paper cites.
P. Kenny, “Bayesian Speaker Verification with Heavy-Tailed Priors,” in in Proceedings of Odyssey
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motliček, Y. Qian, P. Schwarz, et al
2011
Earlier work this paper cites.
D. Garcia-Romero and C. Y. Espy-Wilson, “Analysis of i-vector length normalization in speaker recognition systems,” in Proceedings of Interspeech 2011
2011
Earlier work this paper cites.
O. Plchot, L. Burget, H. Aronowitz, and P. Matějka, “Audio Enhancing With DNN Autoencoder For Speaker Recognition,” in Proceedings of the 41th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016
2016
Earlier work this paper cites.

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P. Matějka and et.al., “BUT-PT system description for nist lre,” in Proceedings of NIST Language Recognition Workshop 2017
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C. Macartney and T. Weyde, “Improved speech enhancement with the wave-u-net,” arXiv preprint arXiv:1811.11307
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2018
Cited alongside, same era.

Then

J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” arXiv preprint arXiv:1806.05622
Original
2018
Later among the works it cites.
M. Diez, L. Burget, and P. Matějka, “Speaker diarization based on bayesian hmm with eigenvoice priors,” in Proceedings of Odyssey 2018, The speaker and Language Recognition Workshop
2018
Later among the works it cites.
N. Ryant, K. Church, C. Cieri, A. Cristia, J. Du, S. Ganapathy, and M. Liberman, “The second dihard diarization challenge: Dataset, task, and baselines,” arXiv preprint arXiv:1906.07839
Original
2019
Later among the works it cites.
F. Landini, S. Wang, M. Diez, L. Burget, P. Matějka, K. Žmolíková, L. Mošner, O. Plchot, O. Novotný, H. Zeinali, and J. Rohdin, “BUT System Description for DIHARD Speech Diarization Challenge 2019,” arXiv preprint arXiv:1910.08847
Original
2019
Later among the works it cites.

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2020

BUT System for the Second DIHARD Speech Diarization Challenge

Landini, Federico, Wang, Shuai, Diez, Mireia et al.

Understand

This paper describes the winning systems developed by the BUT team for the four tracks of the Second DIHARD Speech Diarization Challenge.

For tracks 1 and 2 the systems were mainly based on performing agglomerative hierarchical clustering (AHC) of x-vectors, followed by another x-vector clustering based on Bayes hidden Markov model and variational Bayes inference.
We provide a comparison of the improvement given by each step and share the implementation of the core of the system.
For tracks 3 and 4 with recordings from the Fifth CHiME Challenge, we explored different approaches for doing multi-channel diarization and our best performance was obtained when applying AHC on the fusion of per channel probabilistic linear discriminant analysis scores.

Built on

Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) · 2006
Earlier work this paper cites.
X. Anguera, “Beamformit, the fast and robust acoustic beamformer,” 2006
2006
Earlier work this paper cites.
P. Kenny, “Bayesian analysis of speaker diarization with eigenvoice priors,” tech. rep., Montreal: CRIM, 2008
2008
Earlier work this paper cites.
T. Nakatani, T. Yoshioka, K. Kinoshita, M. Miyoshi, and B. H. Juang, “Speech dereverberation based on variance-normalized delayed linear prediction,” IEEE Transactions on Audio, Speech, and Language Processing
2010
Earlier work this paper cites.
P. Kenny, “Bayesian Speaker Verification with Heavy-Tailed Priors,” in in Proceedings of Odyssey
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motliček, Y. Qian, P. Schwarz, et al
2011
Earlier work this paper cites.
D. Garcia-Romero and C. Y. Espy-Wilson, “Analysis of i-vector length normalization in speaker recognition systems,” in Proceedings of Interspeech 2011
2011
Earlier work this paper cites.
O. Plchot, L. Burget, H. Aronowitz, and P. Matějka, “Audio Enhancing With DNN Autoencoder For Speaker Recognition,” in Proceedings of the 41th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 2016
2016
Earlier work this paper cites.

Similar

P. Matějka and et.al., “BUT-PT system description for nist lre,” in Proceedings of NIST Language Recognition Workshop 2017
Original
2017
Cited alongside, same era.
N. Ryant, K. Church, C. Cieri, A. Cristia, J. Du, S. Ganapathy, and M. Liberman, “First dihard challenge evaluation plan,” 2018
2018
Cited alongside, same era.
J. Barker, S. Watanabe, E. Vincent, and J. Trmal, “The fifth ’chime’ speech separation and recognition challenge: Dataset, task and baselines,” arXiv preprint arXiv:1803.10609
Original
2018
Cited alongside, same era.
C. Macartney and T. Weyde, “Improved speech enhancement with the wave-u-net,” arXiv preprint arXiv:1811.11307
Original
2018
Cited alongside, same era.

Then

J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” arXiv preprint arXiv:1806.05622
Original
2018
Later among the works it cites.
M. Diez, L. Burget, and P. Matějka, “Speaker diarization based on bayesian hmm with eigenvoice priors,” in Proceedings of Odyssey 2018, The speaker and Language Recognition Workshop
2018
Later among the works it cites.
N. Ryant, K. Church, C. Cieri, A. Cristia, J. Du, S. Ganapathy, and M. Liberman, “The second dihard diarization challenge: Dataset, task, and baselines,” arXiv preprint arXiv:1906.07839
Original
2019
Later among the works it cites.
F. Landini, S. Wang, M. Diez, L. Burget, P. Matějka, K. Žmolíková, L. Mošner, O. Plchot, O. Novotný, H. Zeinali, and J. Rohdin, “BUT System Description for DIHARD Speech Diarization Challenge 2019,” arXiv preprint arXiv:1910.08847
Original
2019
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. Drude, J. Heymann, C. Boeddeker, and R. Haeb-Umbach, “NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing,” in 13. ITG Fachtagung Sprachkommunikation

2018

Cited alongside, same era.

M. Diez, F. Landini, L. Burget, J. Rohdin, A. Silnova, K. Žmolíková, O. Novotný, K. Veselý, O. Glembek, O. Plchot, et al

2018

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 Interspeech

2018

Cited alongside, same era.

L. Burget, M. Diez, S. Wang, and F. Landini, “VBHMM x-vectors Diarization (aka VBx).” https://speech.fit.vutbr.cz/software/vbhmm-x-vectors-diarization

Cited in the paper.

Kaldi, “SRE16 v2.” https://github.com/kaldi-asr/kaldi/tree/master/egs/sre16/v2

Cited in the paper.

M. Diez, L. Burget, S. Wang, J. Rohdin, and H. Černocký, “Bayesian HMM based x-vector clustering for Speaker Diarization,” in Proceedings of Interspeech

2019

Later among the works it cites.

M. Diez, L. Burget, F. Landini, and H. Černocký, “Analysis of speaker diarization based on bayesian hmm with eigenvoice priors,” IEEE/ACM Transactions on Audio, Speech, and Language Processing

2019

Later among the works it cites.

M. Diez, L. Burget, F. Landini, S. Wang, and H. Černocký, “Optimizing Bayesian HMM based x-vector clustering for the second DIHARD speech diarization challenge,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2020

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