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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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Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks., , , and . CoRR, (2020)Adaptation Approaches for Pronunciation Scoring with Sparse Training Data., , and . SPECOM, volume 10458 of Lecture Notes in Computer Science, page 87-97. Springer, (2017)From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization., , , and . INTERSPEECH, page 5095-5099. ISCA, (2022)But System for the Second Dihard Speech Diarization Challenge., , , , , , , , , and 2 other author(s). ICASSP, page 6529-6533. IEEE, (2020)Discriminative Training of VBx Diarization., , , , , , and . CoRR, (2023)DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors., , , and . CoRR, (2023)Do End-to-End Neural Diarization Attractors Need to Encode Speaker Characteristic Information?, , , , , and . CoRR, (2024)DiaCorrect: Error Correction Back-end For Speaker Diarization., , , , , , , and . CoRR, (2023)BUT System for DIHARD Speech Diarization Challenge 2018., , , , , , , , , and 2 other author(s). INTERSPEECH, page 2798-2802. ISCA, (2018)Analysis of the BUT Diarization System for VoxConverse Challenge., , , , , , and . CoRR, (2020)