Purity Algorithms for Speaker Diarization of Meetings Data

When performing speaker diarization, it is common to use an agglomerative clustering approach where the acoustic data is first split in small pieces and then pairs are merged until reaching a stopping point. When using a purely agglomerative clustering technique, one cluster cannot be split into two. Therefore, errors caused by multiple speakers being assigned to one cluster can be common. Furthermore, clusters often contain non-speech frames, creating problems when deciding which two clusters to merge and when to stop the clustering. In this paper, we present two algorithms that aim to purify the clusters. The first assigns conflicting speech segments to a new cluster, and the second detects and eliminates non-speech frames when comparing two clusters. We show improvements of over 18% relative using three datasets from the most current rich transcription (RT) evaluations