Cluster criterion functions in spectral subspace and their application in speaker clustering

In this paper, we propose two cluster criterion functions which aim to maximize the separation between intra-cluster distances and inter-cluster distances. These criteria can automatically deduce the desired number of clusters based on their extremized values. We then propose an algorithm to apply our criterion functions in conjunction with spectral clustering. By exploiting the characteristic of spectral subspace, we show that the speakers are more separable in this subspace which will further enhance the effectiveness of our proposed criteria. The algorithm is used in our agglomerative hierarchical speaker diarization system to test on Rich Transcription 2007 conference data set and obtains very good results.