| Front-end factor analysis for speaker verification N Dehak, PJ Kenny, R Dehak, P Dumouchel, P Ouellet IEEE Transactions on Audio, Speech, and Language Processing 19 (4), 788-798, 2010 | 4922 | 2010 |
| Language recognition via i-Vectors and dimensionality reduction. N Dehak, PA Torres-Carrasquillo, DA Reynolds, R Dehak Interspeech, 857-860, 2011 | 593 | 2011 |
| Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification N Dehak, R Dehak, P Kenny, N Brümmer, P Ouellet, P Dumouchel Tenth Annual conference of the international speech communication association, 2009 | 496 | 2009 |
| Cosine similarity scoring without score normalization techniques. N Dehak, R Dehak, JR Glass, DA Reynolds, P Kenny Odyssey 15, 2010 | 273 | 2010 |
| Unsupervised methods for speaker diarization: An integrated and iterative approach SH Shum, N Dehak, R Dehak, JR Glass IEEE Transactions on Audio, Speech, and Language Processing 21 (10), 2015-2028, 2013 | 196 | 2013 |
| Support vector machines and joint factor analysis for speaker verification N Dehak, P Kenny, R Dehak, O Glembek, P Dumouchel, L Burget, ... 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 140 | 2009 |
| State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and speakers in the wild evaluations J Villalba, N Chen, D Snyder, D Garcia-Romero, A McCree, G Sell, ... Computer Speech & Language 60, 101026, 2020 | 129 | 2020 |
| State-of-the-Art Speaker Recognition for Telephone and Video Speech: The JHU-MIT Submission for NIST SRE18. J Villalba, N Chen, D Snyder, D Garcia-Romero, A McCree, G Sell, ... Interspeech, 1488-1492, 2019 | 117 | 2019 |
| Unsupervised speaker adaptation based on the cosine similarity for text-independent speaker verification. S Shum, N Dehak, R Dehak, JR Glass Odyssey 16, 2010 | 95 | 2010 |
| Cepstral and long-term features for emotion recognition. P Dumouchel, N Dehak, Y Attabi, R Dehak, N Boufaden Interspeech, 344-347, 2009 | 72 | 2009 |
| First attempt of boltzmann machines for speaker verification M Senoussaoui, N Dehak, P Kenny, R Dehak, P Dumouchel Odyssey 2012-the speaker and language recognition workshop, 2012 | 62 | 2012 |
| A channel-blind system for speaker verification N Dehak, ZN Karam, DA Reynolds, R Dehak, WM Campbell, JR Glass 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 51 | 2011 |
| Linear and non linear kernel GMM supervector machines for speaker verification. R Dehak, N Dehak, P Kenny, P Dumouchel InterSpeech, 302-305, 2007 | 43 | 2007 |
| Kernel combination for SVM speaker verification. R Dehak, N Dehak, P Kenny, P Dumouchel Odyssey, 21, 2008 | 28 | 2008 |
| Comparison between factor analysis and GMM support vector machines for speaker verification. N Dehak, R Dehak, P Kenny, P Dumouchel Odyssey, 9, 2008 | 25 | 2008 |
| The jhu-mit system description for nist sre18 J Villalba, N Chen, D Snyder, D Garcia-Romero, A McCree, G Sell, ... Johns Hopkins University, Baltimore, MD, Tech. Rep, 2018 | 23 | 2018 |
| The MIT-LL, JHU and LRDE NIST 2016 Speaker Recognition Evaluation System. PA Torres-Carrasquillo, F Richardson, SC Nercessian, DE Sturim, ... Interspeech, 1333-1337, 2017 | 19 | 2017 |
| The role of speaker factors in the NIST extended data task. P Kenny, N Dehak, R Dehak, V Gupta, P Dumouchel Odyssey, 11, 2008 | 14 | 2008 |
| Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot XF Cadet, O Lo-Thong, S Bureau, R Dehak, M Bessafi Scientific Reports 9 (1), 19197, 2019 | 11 | 2019 |
| Label-efficient self-supervised speaker verification with information maximization and contrastive learning T Lepage, R Dehak arXiv preprint arXiv:2207.05506, 2022 | 10 | 2022 |
