Recent Improvements on ILP-based Clustering for Broadcast News Speaker Diarization
|Grégor Dupuy, Sylvain Meignier, Paul Deléglise and Yannick Estève|
First we propose a reformulation of the Integer Linear Programming (ILP) clustering method we introduced at Odyssey 2012, for broadcast news Speaker Diarization. We included an overall distance filtering which drastically reduce the complexity of the problems to be solved. Then, we present a clustering approach where the problem is globally considered as a connected graph. The search for Star-graph sub-components allows the system to solve almost the whole clustering problem: only 8 of the 28 shows that compose the January 2013 test corpus of the REPERE 2012 French evaluation campaign, on which the experiments were conducted, were processed with the ILP clustering. Compared to the original formulation of the ILP clustering problem, our contribution lead to a reduction of the number of variables in the ILP problem, from 1743 to 53 on average, and a reduction of the number of constraints, from 3449 to 53 on average. The graph content clustering method appears to be an interesting alternative to the current clustering methods, since its results are better than that of the state of the art approaches like GMM-based HAC (15.18% against 16.22% DER).