Active Selection with Label Propagation for Minimizing Human Effort in Speaker Annotation of TV ShowsReport as inadecuate




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1 LIG - Laboratoire d-Informatique de Grenoble 2 MRIM - Modélisation et Recherche d’Information Multimédia Grenoble LIG - Laboratoire d-Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique

Abstract : In this paper an approach minimizing the human involvement in the manual annotation of speakers is presented. At each iter- ation a selection strategy choses the most suitable speech track for manual annotation, which is then associated with all the tracks in the cluster that contains it. The study makes use of a system that propagates the speaker track labels. This is done using a agglomerative clustering with constraints. Several dif- ferent unsupervised active learning selection strategies are eval- uated. Additionally, the presented approach can be used to ef- ficiently generate sets of speech tracks for training biometric models. In this case both the length of the speech track for a given person and its purity are taken into consideration. To evaluate the system the REPERE video corpus was used. Along with the speech tracks extracted from the videos, the op- tical character recognition system was adapted to extract names of potential speakers. This was then used as the -cold start- for the selection method.

Keywords : active learning annotation propagation clustering speaker identification





Author: Budnik Mateusz - Johann Poignant - Laurent Besacier - Georges Quénot -

Source: https://hal.archives-ouvertes.fr/



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