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Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge.

, , , , , , , , , , and . INTERSPEECH, page 2808-2812. ISCA, (2018)

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Measuring Uncertainty in Deep Regression Models: The Case of Age Estimation from Speech., , , and . ICASSP, page 4939-4943. IEEE, (2018)JHU Diarization System Description., , , , and . IberSPEECH, page 236-239. ISCA, (2018)State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and Speakers in the Wild evaluations., , , , , , , , , and 2 other author(s). Computer Speech & Language, (2020)ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks., , , and . CoRR, (2019)Tied Variational Autoencoder Backends for i-Vector Speaker Recognition., , and . INTERSPEECH, page 1004-1008. ISCA, (2017)Language model integration based on memory control for sequence to sequence speech recognition., , , , , , and . CoRR, (2018)A forced gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing., , , , , , and . Biomed. Signal Proc. and Control, (2019)Investigation on Bandwidth Extension for Speaker Recognition., , , and . INTERSPEECH, page 1111-1115. ISCA, (2018)An Investigation of Non-linear i-vectors for Speaker Verification., , and . INTERSPEECH, page 87-91. ISCA, (2018)Joint Verification-Identification in end-to-end Multi-Scale CNN Framework for Topic Identification., , and . ICASSP, page 6199-6203. IEEE, (2018)