On the Use of Dot Scoring for Speaker Diarization

Abstract

In this paper, an alternative dot scoring based agglomerative hierarchical clustering approach for speaker diarization is presented. Dot-scoring is a simple and fast technique used in speaker verification that makes use of a linearized procedure to score test segments against target models. In our speaker diarization approach speech segments are represented by MAP-adapted GMM zero and first order statistics, dot scoring is applied to compute a similarity measure between segments (or clusters) and finally an agglomerative clustering algorithm is applied until no pair of clusters exceeds a similarity threshold. This diarization system was developed for the Albayzin 2010 Speaker Diarization Evaluation on broadcast news. Results show that the lowest error rate that the clustering algorithm could attain for the evaluation set was around 20% and that over-segmentation was the main source of degradation, due to the lack of robustness in the estimation of statistics for short segments.

This work has been supported by the University of the Basque Country under Grant GIU10/18 and the Government of the Basque Country, under program SAIOTEK (project S-PE10UN87), and the Spanish MICINN, under Plan Nacional de I+D+i (project TIN2009-07446, partially financed by FEDER funds).