The SAIL speaker diarization system for analysis of spontaneous meetings

In this paper, we propose a novel approach to speaker diarization of spontaneous meetings in our own multimodal SmartRoom environment. The proposed speaker diarization system first applies a sequential clustering concept to segmentation of a given audio data source, and then performs agglomerative hierarchical clustering for speaker-specific classification (or speaker clustering) of speech segments. The speaker clustering algorithm utilizes an incremental Gaussian mixture cluster modeling strategy, and a stopping point estimation method based on information change rate. Through experiments on various meeting conversation data of approximately 200 minutes total length, this system is demonstrated to provide diarization error rate of 18.90% on average.