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Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge.

, , , , and . ICASSP, page 6519-6523. IEEE, (2020)

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On the Projection of PLLRs for Unbounded Feature Distributions in Spoken Language Recognition., , , , and . IEEE Signal Process. Lett., 21 (9): 1073-1077 (2014)Analysis of the BUT Diarization System for VoxConverse Challenge., , , , , , and . CoRR, (2020)Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization., , , and . ICASSP, page 1-5. IEEE, (2023)The Albayzin 2010 Language Recognition Evaluation., , , , and . INTERSPEECH, page 1529-1532. ISCA, (2011)Analysis of BUT-PT Submission for NIST LRE 2017., , , , , , , , , and 5 other author(s). Odyssey, page 47-53. ISCA, (2018)Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge., , , , and . ICASSP, page 6519-6523. IEEE, (2020)End-to-End DNN Based Speaker Recognition Inspired by I-Vector and PLDA., , , , , and . ICASSP, page 4874-4878. IEEE, (2018)High-performance Query-by-Example Spoken Term Detection on the SWS 2013 evaluation., , , , and . ICASSP, page 7819-7823. IEEE, (2014)On the use of phone log-likelihood ratios as features in spoken language recognition., , , , and . SLT, page 274-279. IEEE, (2012)KALAKA-3: a database for the assessment of spoken language recognition technology on YouTube audios., , , , and . Lang. Resour. Evaluation, 50 (2): 221-243 (2016)