An Algorithm of Speaker Clustering Based on Model Distance

Abstract

An algorithm based on Model Distance (MD) for spectral speaker clustering is proposed to deal with the shortcoming of general spectral clustering algorithm in describing the distribution of signal source. First, an Universal Background Model (UBM) is created with a large quantity of independent speakers; Then, Gaussian Mixture Model (GMM) is trained from the UBM for every speech segment; At last, the probability distance between the GMM of every speech segment is used to build affinity matrix, and speaker spectral clustering is done on the affinity matrix. Experimental results based on news and conference data sets show that an average of 6.38% improvements in F measure is obtained in comparison with algorithm based on the feature vector distance. In addition, the proposed algorithm is 11.72 times faster.