A Cluster Purification Algorithm for Speaker Diarization System

In speaker diarization system, it´s common to use bottom-up clustering method where the input data is first split in small pieces and then merged the most similar segments until reaching a stopping point. However, it´s not easy to ensure that every selection merges the right pair, and these errors tend to deteriorate the post-merging results. In this paper, a fast cluster purification algorithm is introduced after the size of clusters reach to a pre-estimated number K, which is no less than the real number of speakers involved in the conversation, and try to remedy the errors by removing the inappropriate segments into the right cluster. An effective way to estimate the number of speakers is also introduced before the clustering stage. The experiment results show improvement in both the purity of cluster and the diarization error rate (DER) after using the purification algorithm. The purity improves 0.8% and DER reduces 1.14% in average.