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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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Hidden Markov models for automatic annotation and content-based retrieval of images and video., , and . SIGIR, page 544-551. ACM, (2005)Limited resource term detection for effective topic identification of speech., and . ICASSP, page 7118-7122. IEEE, (2014)Speaker Recognition for Multi-speaker Conversations Using X-vectors., , , , , and . ICASSP, page 5796-5800. IEEE, (2019)Combining local and broad topic context to improve term detection., and . SLT, page 442-447. IEEE, (2014)Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR., , , and . IEEE ACM Trans. Audio Speech Lang. Process., 26 (11): 1949-1961 (2018)Computation of Csiszár's mutual Information of order α., , and . ISIT, page 2106-2110. IEEE, (2008)Recurrent neural network based language model, , , , and . Interspeech, page 1045--1048. (2010)Transliteration of proper names in cross-language applications., and . SIGIR, page 365-366. ACM, (2003)Learning From Flawed Data: Weakly Supervised Automatic Speech Recognition., , , , , and . ASRU, page 1-8. IEEE, (2023)Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora., , and . WMT@EMNLP, page 1100-1109. Association for Computational Linguistics, (2021)