Before revision:  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.
After revision:  A well trained neural network based speech enhancement model is used for pre-processing and a neural network 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 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.