Fetching the paper…
Reading the bibliography…
Receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are two widely used evaluation metrics for speaker verification.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Proc. Interspeech 2018 , 2018, pp. 1086–1090. [Online]. Available: http://dx.doi.org/10.21437/Interspeech.2018-1929
1929
Earlier work this paper cites.
A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, “The det curve in assessment of detection task performance,” National Inst of Standards and Technology Gaithersburg MD, Tech. Rep., 1997
1997
Earlier work this paper cites.
D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, “Speaker verification using adapted gaussian mixture models,” Digital signal processing , vol. 10, no. 1-3, pp. 19–41, 2000
2000
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.
A. O. Hatch, S. Kajarekar, and A. Stolcke, “Within-class covariance normalization for svm-based speaker recognition,” in Ninth international conference on spoken language processing , 2006
2006
Earlier work this paper cites.
N. Brümmer and J. Du Preez, “Application-independent evaluation of speaker detection,” Computer Speech & Language , vol. 20, no. 2-3, pp. 230–275, 2006
2006
Earlier work this paper cites.
J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Information-theoretic metric learning,” in Proceedings of the 24th international conference on Machine learning . ACM, 2007, pp. 209–216
2007
Earlier work this paper cites.
K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” Journal of Machine Learning Research , vol. 10, no. Feb, pp. 207–244, 2009
2009
Earlier work this paper cites.
P. Kenny, “Bayesian speaker verification with heavy-tailed priors.” in Odyssey , 2010, p. 14
2010
Earlier work this paper cites.
N. Brummer, “Measuring, refining and calibrating speaker and language information extracted from speech,” Ph.D. dissertation, Stellenbosch: University of Stellenbosch, 2010
2010
Earlier work this paper cites.
N. Brümmer and E. De Villiers, “The speaker partitioning problem.” in Odyssey , 2010, p. 34
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,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788–798, 2011
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 Twelfth Annual Conference of the International Speech Communication Association , 2011
2011
Earlier work this paper cites.
L. Burget, O. Plchot, S. Cumani, O. Glembek, P. Matějka, and N. Brümmer, “Discriminatively trained probabilistic linear discriminant analysis for speaker verification,” in 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2011, pp. 4832–4835
2011
Earlier work this paper cites.
S. Cumani, N. Brümmer, L. Burget, and P. Laface, “Fast discriminative speaker verification in the i-vector space,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2011, pp. 4852–4855
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. EPFL-CONF-192584. IEEE Signal Processing Society, 2011
2011
Earlier work this paper cites.
L. P. Garcia-Perera, J. A. Nolazco-Flores, B. Raj, and R. Stern, “Optimization of the det curve in speaker verification,” in 2012 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2012, pp. 318–323
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
B. Kulis, “Metric learning: A survey,” Foundations and Trends in Machine Learning , vol. 5, no. 4, pp. 287–364, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2013
Cited alongside, same era.
Y. Lei, N. Scheffer, L. Ferrer, and M. McLaren, “A novel scheme for speaker recognition using a phonetically-aware deep neural network,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on . IEEE, 2014, pp. 1695–1699
2014
Cited alongside, same era.
P. Kenny, V. Gupta, T. Stafylakis, P. Ouellet, and J. Alam, “Deep neural networks for extracting baum-welch statistics for speaker recognition,” in Proc. Odyssey , 2014, pp. 293–298
2014
Cited alongside, same era.
E. Variani, X. Lei, E. McDermott, I. Lopez-Moreno, and J. Gonzalez-Dominguez, “Deep neural networks for small footprint text-dependent speaker verification.” in ICASSP , vol. 14. Citeseer, 2014, pp. 4052–4056
2014
Cited alongside, same era.
S. Cumani and P. Laface, “Joint estimation of plda and nonlinear transformations of speaker vectors,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 25, no. 10, pp. 1890–1900, 2017
2017
Later among the works it cites.
A. N. snd Joon Son Chung and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in Proc. Interspeech 2017 , 2017, pp. 1487–1491
2017
Later among the works it cites.
J.-W. Jung, H.-S. Heo, I.-H. Yang, H.-J. Shim, and H.-J. Yu, “A complete end-to-end speaker verification system using deep neural networks: From raw signals to verification result,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5349–5353
2018
Later among the works it cites.
S. Cumani and P. Laface, “Speaker recognition using e–vectors,” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) , vol. 26, no. 4, pp. 736–748, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Cumani and P. Laface, “Large-scale training of pairwise support vector machines for speaker recognition,” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) , vol. 22, no. 11, pp. 1590–1600, 2014
2014
Cited alongside, same era.
O. Ghahabi and J. Hernando, “Deep belief networks for i-vector based speaker recognition,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on . IEEE, 2014, pp. 1700–1704
2014
Cited alongside, same era.
F. Richardson, D. Reynolds, and N. Dehak, “Deep neural network approaches to speaker and language recognition,” IEEE Signal Processing Letters , vol. 22, no. 10, pp. 1671–1675, 2015
2015
Cited alongside, same era.
E. Hoffer and N. Ailon, “Deep metric learning using triplet network,” in International Workshop on Similarity-Based Pattern Recognition . Springer, 2015, pp. 84–92
2015
Cited alongside, same era.
G. Heigold, I. Moreno, S. Bengio, and N. Shazeer, “End-to-end text-dependent speaker verification,” in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on . IEEE, 2016, pp. 5115–5119
2016
Cited alongside, same era.
S.-X. Zhang, Z. Chen, Y. Zhao, J. Li, and Y. Gong, “End-to-end attention based text-dependent speaker verification,” in Spoken Language Technology Workshop (SLT), 2016 IEEE . IEEE, 2016, pp. 171–178
2016
Cited alongside, same era.
D. Snyder, P. Ghahremani, D. Povey, D. Garcia-Romero, Y. Carmiel, and S. Khudanpur, “Deep neural network-based speaker embeddings for end-to-end speaker verification,” in Spoken Language Technology Workshop (SLT), 2016 IEEE . IEEE, 2016, pp. 165–170
2016
Cited alongside, same era.
H. Oh Song, Y. Xiang, S. Jegelka, and S. Savarese, “Deep metric learning via lifted structured feature embedding,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4004–4012
2016
Cited alongside, same era.
Z. Tan, M.-W. Mak, B. K.-W. Mak, and Y. Zhu, “Denoised senone i-vectors for robust speaker verification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 26, no. 4, pp. 820–830, 2018
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 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Zhu, T. Ko, D. Snyder, B. Mak, and D. Povey, “Self-attentive speaker embeddings for text-independent speaker verification,” Proc. Interspeech 2018 , pp. 3573–3577, 2018
2018
Later among the works it cites.
Z. Gao, Y. Song, I. McLoughlin, W. Guo, and L. Dai, “An improved deep embedding learning method for short duration speaker verification,” Proc. Interspeech 2018 , pp. 3578–3582, 2018
2018
Later among the works it cites.
S. Yadav and A. Rai, “Learning discriminative features for speaker identification and verification,” Proc. Interspeech 2018 , pp. 2237–2241, 2018
2018
Later among the works it cites.
N. Li, D. Tuo, D. Su, Z. Li, D. Yu, and A. Tencent, “Deep discriminative embeddings for duration robust speaker verification,” Proc. Interspeech 2018 , pp. 2262–2266, 2018
2018
Later among the works it cites.
C. Zhang, K. Koishida, and J. H. Hansen, “Text-independent speaker verification based on triplet convolutional neural network embeddings,” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) , vol. 26, no. 9, pp. 1633–1644, 2018
2018
Later among the works it cites.
——, “Scoring heterogeneous speaker vectors using nonlinear transformations and tied plda models,” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) , vol. 26, no. 5, pp. 995–1009, 2018
2018
Later among the works it cites.
Z. Tieran, H. Jiqing, and Z. Guibin, “Deep neural network based discriminative training for i-vector/plda speaker verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5354–5358
2018
Later among the works it cites.
Z. Bai, X.-L. Zhang, and J. Chen, “Cosine metric learning for speaker verification in the i-vector space,” Proc. Interspeech 2018 , pp. 1126–1130, 2018
2018
Later among the works it cites.
S. Novoselov, V. Shchemelinin, A. Shulipa, A. Kozlov, and I. Kremnev, “Triplet loss based cosine similarity metric learning for text-independent speaker recognition,” Proc. Interspeech 2018 , pp. 2242–2246, 2018
2018
Later among the works it cites.
J. Huo, Y. Gao, Y. Shi, and H. Yin, “Cross-modal metric learning for auc optimization,” IEEE Transactions on Neural Networks and Learning Systems, PP (99) , pp. 1–13, 2018
2018
Later among the works it cites.
F. Cakir, K. He, X. Xia, B. Kulis, and S. Sclaroff, “Deep metric learning to rank,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 1861–1870
2019
Closest in time.