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In this paper, we present an approach for minimizing human effort in manual speaker annotation. Label propagation is used at each iteration of an active learning cycle. More precisely, a selection strategy for choosing the most suitable speech track to be labeled is proposed. Four different selection strategies are evaluated and all the tracks in a corresponding cluster are gathered using agglomerative clustering in order to propagate human annotations. To further reduce the manual labordoi:10.1109/icassp.2016.7472743 dblp:conf/icassp/BudnikBKD16 fatcat:k6a7xeihzfflvg4xkfa52svuri