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MagNetO: X-vector Magnitude Estimation Network plus Offset for Improved Speaker Recognition.

, , and . Odyssey, page 1-8. ISCA, (2020)

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Using quality measures for multilevel speaker recognition., , , and . Comput. Speech Lang., 20 (2-3): 192-209 (2006)Recent Developments on Espnet Toolkit Boosted By Conformer., , , , , , , , , and 5 other author(s). ICASSP, page 5874-5878. IEEE, (2021)Speaker Diarization Using Leave-One-Out Gaussian PLDA Clustering of DNN Embeddings., , and . INTERSPEECH, page 381-385. ISCA, (2019)VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge., , , , , , and . CoRR, (2023)Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge., , , , , , , , , and 1 other author(s). INTERSPEECH, page 2808-2812. ISCA, (2018)Robust likelihood ratio estimation in Bayesian forensic speaker recognition., , , , and . INTERSPEECH, page 693-696. ISCA, (2003)State-of-the-Art Speaker Recognition for Telephone and Video Speech: The JHU-MIT Submission for NIST SRE18., , , , , , , , , and 6 other author(s). INTERSPEECH, page 1488-1492. ISCA, (2019)MagNetO: X-vector Magnitude Estimation Network plus Offset for Improved Speaker Recognition., , and . Odyssey, page 1-8. ISCA, (2020)Script Identification using Across- and Within-Image Distribution Estimation., , , and . ICDAR, page 1084-1089. IEEE, (2019)State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and Speakers in the Wild evaluations., , , , , , , , , and 2 other author(s). Comput. Speech Lang., (2020)