Speaker Tracking by Anchor Models Using Speaker Segment Cluster Information

In this paper, we present a speaker tracking system entirely based on anchor models approach. The aim of this article is to evaluate if the probabilistic anchor models approach, which models a speaker by a normal distribution in the anchor models space, gives good performances in speaker tracking and also to investigate how speaker segment cluster information can improve speaker tracking performances. Evaluation is done on the audio database of the ESTER evaluation campaign for the rich transcription of French broadcast news. Results show that deterministic metrics on anchor models are suitable for segmentation and clustering tasks, whereas the probabilistic approach on anchor models gives interesting results for speaker-tracking. It is also observed that tracking performances are improved when all segments of a cluster are pooled together prior to the classification process. This improvement manifests itself as an improved recall rate on short segments