| Paper: | MMSP-P3.6 | ||
| Session: | Multimedia Database, Content Retrieval, Joint Processing and Standards | ||
| Time: | Wednesday, May 17, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Multimedia Signal Processing: Multimedia database and digital libraries | ||
| Title: | Fast Incremental Clustering of Gaussian Mixture Speaker Models for Scaling up Retrieval in On-line Broadcast | ||
| Authors: | Jamal Eddine Rougui, Mohammed Rziza, Driss Aboutajdine, GSCM, Faculté des Sciences Rabat, Morocco; Marc Gelgon, Jose Martinez, LINA, France | ||
| Abstract: | In this paper, we introduce a hierarchical classification approach in the incremental framework of speaker indexing. The technique of incremental generation of speaker-homogeneous segments is applied in the first phase. Then, we propose a hierarchical classification approach that applied in the speaker indexing framework. This approach benefits from the efficiency of Gaussian mixture model (GMM) merge algorithm to the high accuracy of update Gaussian mixture models which referenced by speakers tree index. The adaptive threshold algorithm reduces the cost of exploring the speakers GMM into the balanced binary tree of speaker's index, whose complexity becomes logarithmic curve. | ||
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