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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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JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS., , , , , and . ASRU, page 539-546. IEEE, (2015)Morpheme-Based Language Modeling for Arabic Lvcsr., , , and . ICASSP (1), page 1053-1056. IEEE, (2006)Minimum Phone Error and I-smoothing for improved discriminative training., and . ICASSP, page 105-108. IEEE, (2002)Discriminative map for acoustic model adaptation., , and . ICASSP (1), page 312-315. IEEE, (2003)A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models., , , and . SLT, page 250-257. IEEE, (2018)DOVER-Lap: A Method for Combining Overlap-aware Diarization Outputs., , , , , , and . CoRR, (2020)The subspace Gaussian mixture model - A structured model for speech recognition., , , , , , , , , and 3 other author(s). Comput. Speech Lang., 25 (2): 404-439 (2011)Automatic transcription of conversational telephone speech., , , , , , , and . IEEE Trans. Speech Audio Process., 13 (6): 1173-1185 (2005)An Alternative to MFCCs for ASR., , , , and . INTERSPEECH, page 1664-1667. ISCA, (2020)Efficient MDI Adaptation for n-Gram Language Models., , , , and . INTERSPEECH, page 4916-4920. ISCA, (2020)