This paper describes the development of our system for the VoxCeleb Speaker Diarisation Challenge 2020. A well trained neural network based speech enhancement model is used for pre-processing and a neural network based voice activity detection (VAD) system is followed to remove background musicand noisewhich are harmful for speaker diarisation system. The following diarisation system is built based on agglomerative hierarchical clustering (AHC) of x-vectors and a variational Bayesian hidden Markov Model (VB-HMM) based iterative clustering  . Experimental results demonstrate that the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
This paper describes system setup of our submission to speaker diarisation track (Track 4) of VoxCeleb Speaker Recognition Challenge 2020. Our diarisation system consists of a well-trained neural network based speech enhancement model as pre-processing front-end of input speech signals. We replace conventional energy-based voice activity detection (VAD) with a neural network based VAD. The neural network based VAD provides more accurate annotation of speech segments containing only background music, noise, and other interference, which is crucial to diarisation performance. We apply agglomerative hierarchical clustering (AHC) of x-vectors and variational Bayesian hidden Markov model (VB-HMM) based iterative clustering for speaker clustering . Experimental results demonstrate that our proposed system achieves substantial improvements over the baseline system, yielding diarisation error rate (DER) of 10.45\%, and Jacard error rate (JER) of 22.46\% on the evaluation set.
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the development of our system for the VoxCeleb Speaker Diarisation
system setup of our submission to speaker diarisation track (Track 4) of VoxCeleb Speaker Recognition
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system is followed to remove background musicand noisewhich are harmful for speaker diarisation system. The following diarisation system is built based on
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the proposed system yields substantial improvements compared with the baseline method for the diarisation task of the VoxCeleb Speaker Recognition Challenge 2020.
our proposed system achieves substantial improvements over the baseline system, yielding diarisation error rate (DER) of 10.45\%, and Jacard error rate (JER) of 22.46\% on the evaluation set.