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Speaker diarization is an essential step for processing multi-speaker audio.
R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,”
1989
Earlier work this paper cites.
“2000 NIST Speaker Recognition Evaluation,”
2000
Earlier work this paper cites.
A. Janin, D. Baron, J. Edwards, D. Ellis, D. Gelbart, N. Morgan, B. Peskin, T. Pfau, E. Shriberg, A. Stolcke, and C. Wooters, “The ICSI meeting corpus,” in
2003
Earlier work this paper cites.
S. E. Tranter and D. A. Reynolds, “An overview of automatic speaker diarization systems,”
2006
Earlier work this paper cites.
Ö. Çetin and E. Shriberg, “Overlap in meetings: ASR effects and analysis by dialog factors, speakers, and collection site,” in
2006
Earlier work this paper cites.
S. Renals, T. Hain, and H. Bourlard, “Interpretation of multiparty meetings the AMI and Amida projects,” in
2008
Earlier work this paper cites.
T. Mikolov, M. Karafiát, L. Burget, J. Černocký, and S. Khudanpur, “Recurrent neural network based language model,” in
2010
Earlier work this paper cites.
N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,”
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, J. Silovsky, G. Stemmer, and K. Vesely, “The Kaldi speech recognition toolkit,” in
2011
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,”
2012
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,”
2013
Earlier work this paper cites.
G. Sell and D. Garcia-Romero, “Speaker diarization with PLDA i-vector scoring and unsupervised calibration,” in
2014
Earlier work this paper cites.
M. Senoussaoui, P. Kenny, T. Stafylakis, and P. Dumouchel, “A study of the cosine distance-based mean shift for telephone speech diarization,”
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in
2014
Cited alongside, same era.
J. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: Discriminative embeddings for segmentation and separation,” in
2016
Cited alongside, same era.
D. Dimitriadis and P. Fousek, “Developing on-line speaker diarization system,” in
2017
Cited alongside, same era.
D. Garcia-Romero, D. Snyder, G. Sell, D. Povey, and A. McCree, “Speaker diarization using deep neural network embeddings,” in
2017
Cited alongside, same era.
D. Yu, M. Kolbæk, Z. Tan, and J. Jensen, “Permutation invariant training of deep models for speaker-independent multi-talker speech separation,” in
2017
Cited alongside, same era.
Q. Wang, C. Downey, L. Wan, P. A. Mansfield, and I. L. Moreno, “Speaker diarization with LSTM,” in
2018
Later among the works it cites.
L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized end-to-end loss for speaker verification,” in
2018
Later among the works it cites.
D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust DNN embeddings for speaker recognition,” in
2018
Later among the works it cites.
K. Kinoshita, L. Drude, M. Delcroix, and T. Nakatani, “Listening to each speaker one by one with recurrent selective hearing networks,” in
2018
Later among the works it cites.
Y. Luo, Z. Chen, and N. Mesgarani, “Speaker-independent speech separation with deep attractor network,”
2018
Later among the works it cites.
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in
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
2018
Cited alongside, same era.
M. Diez, F. Landini, L. Burget, J. Rohdin, A. Silnova, K. Z̆molíková, O. Novotný, K. Veselý, O. Glembek, O. Plchot, L. Mos̆ner, and P. Matĕjka, “BUT system for DIHARD speech diarization challenge 2018,” in
2018
Cited alongside, same era.
L. Sun, J. Du, C. Jiang, X. Zhang, S. He, B. Yin, and C.-H. Lee, “Speaker diarization with enhancing speech for the first DIHARD challenge,” in
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,” in
2018
Cited alongside, same era.
C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann, and R. Haeb-Umbach, “Front-End Processing for the CHiME-5 Dinner Party Scenario,” in
2018
Cited alongside, same era.
N. Kanda, R. Ikeshita, S. Horiguchi, Y. Fujita, K. Nagamatsu, X. Wang, V. Manohar, N. E. Yalta Soplin, M. Maciejewski, S.-J. Chen, A. S. Subramanian, R. Li, Z. Wang, J. Naradowsky, L. P. Garcia-Perera, and G. Sell, “Hitachi/JHU CHiME-5 system: Advances in speech recognition for everyday home environments using multiple microphone arrays,” in
2018
Cited alongside, same era.
N. Kanda, Y. Fujita, S. Horiguchi, R. Ikeshita, K. Nagamatsu, and S. Watanabe, “Acoustic modeling for distant multi-talker speech recognition with single- and multi-channel branches,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Zhang, Q. Wang, Z. Zhu, J. Paisley, and C. Wang, “Fully supervised speaker diarization,” in
2019
Later among the works it cites.
Y. Fujita, N. Kanda, S. Horiguchi, K. Nagamatsu, and S. Watanabe, “End-to-end neural speaker diarization with permutation-free objectives,” in
2019
Later among the works it cites.
Y. Fujita, N. Kanda, S. Horiguchi, Y. Xue, K. Nagamatsu, and S. Watanabe, “End-to-end neural speaker diarization with self-attention,” in
2019
Later among the works it cites.
T. von Neumann, K. Kinoshita, M. Delcroix, S. Araki, T. Nakatani, and R. Haeb-Umbach, “All-neural online source separation, counting, and diarization for meeting analysis,” in
2019
Later among the works it cites.
Z. Huang, S. Watanabe, Y. Fujita, P. Garcia, Y. Shao, D. Poveya, and S. Khudanpur, “Speaker diarization with region proposal network,” in
2020
Closest in time.
S. Horiguchi, Y. Fujita, S. Watanabe, Y. Xue, and K. Nagamatsu, “End-to-end speaker diarization for unknown number of speakers
2020
Closest in time.