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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)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)The JHU Speaker Recognition System for the VOiCES 2019 Challenge., , , , , , and . INTERSPEECH, page 2468-2472. ISCA, (2019)MagNetO: X-vector Magnitude Estimation Network plus Offset for Improved Speaker Recognition., , and . Odyssey, page 1-8. ISCA, (2020)Joint Acoustic and Class Inference for Weakly Supervised Sound Event Detection., , , , , and . ICASSP, page 36-40. IEEE, (2019)A practical two-stage training strategy for multi-stream end-to-end speech recognition., , , , and . CoRR, (2019)Scalable out-of-sample extension of graph embeddings using deep neural networks., , and . Pattern Recognit. Lett., (2017)x-Vector DNN Refinement with Full-Length Recordings for Speaker Recognition., , , , , and . INTERSPEECH, page 1493-1496. ISCA, (2019)An evaluation of graph clustering methods for unsupervised term discovery., , and . INTERSPEECH, page 3209-3213. ISCA, (2015)Analysis of Robustness of Deep Single-Channel Speech Separation Using Corpora Constructed From Multiple Domains., , , , , and . WASPAA, page 165-169. IEEE, (2019)