The Approach of Mean Shift based Cosine Dissimilarity for Multi-Recording Speaker Clustering

Speaker clustering is an important task in many applications such as Speaker Diarization as well as Speech Recognition. Speaker clustering can be done within a single multispeaker recording (Diarization) or for a set of different recordings. In this work we are interested by the former case and we propose a simple iterative Mean Shift (MS) algorithm. MS algorithm is based on Euclidean distance. We propose to use the Cosine distance in order to build a new version of MS algorithm. We report results as measured by speaker and cluster impurities on NIST SRE 2008 datasets.