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The HUAWEI Speaker Diarisation System for the VoxCeleb Speaker Diarisation Challenge
This paper describes system setup of our submission to speaker diarisati...
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Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020
This paper describes the Microsoft speaker diarization system for monaur...
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The NPU System for the 2020 Personalized Voice Trigger Challenge
This paper describes the system developed by the NPU team for the 2020 p...
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ABSP System for The Third DIHARD Challenge
This report describes the speaker diarization system developed by the AB...
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The Speed Submission to DIHARD II: Contributions Lessons Learned
This paper describes the speaker diarization systems developed for the S...
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DIHARD II is Still Hard: Experimental Results and Discussions from the DKU-LENOVO Team
In this paper, we present the submitted system for the second DIHARD Spe...
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Design Choices for X-vector Based Speaker Anonymization
The recently proposed x-vector based anonymization scheme converts any i...
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Analysis of the BUT Diarization System for VoxConverse Challenge
This paper describes the system developed by the BUT team for the fourth track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on the VoxConverse dataset. The system consists of signal pre-processing, voice activity detection, speaker embedding extraction, an initial agglomerative hierarchical clustering followed by diarization using a Bayesian hidden Markov model, a reclustering step based on per-speaker global embeddings and overlapped speech detection and handling. We provide comparisons for each of the steps and share the implementation of the most relevant modules of our system. Our system scored second in the challenge in terms of the primary metric (diarization error rate) and first according to the secondary metric (Jaccard error rate).
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