Robust bootstrapping algorithm of speaker models for on-line unsupervised speaker indexing

A robust bootstrapping framework, which employs Multi-EigenSpace modeling technique based on regression class (RC-MES) to build speaker models with sparse data, and a short-segments clustering to prevent the too short segments from influencing bootstrapping, are proposed in this paper. For a real discussion archived with a total duration of 8 hours, the significant robustness of the proposed method is demonstrated, which not only improves the speaker change detection performance but also outperforms the conventional bootstrapping methods, even if the average bootstrapping segment duration is less than 5 seconds.