Person Instance Graphs for Named Speaker Identification in TV Broadcast
Résumé
We address the problem of named speaker identification in TV broadcast which consists in answering the question " who speaks when? " with the real identity of speakers, using person names automatically obtained from speech transcripts. While existing approaches rely on a first speaker diarization step followed by a local name propagation step to speaker clusters, we propose a unified framework called person instance graph where both steps are jointly modeled as a global optimization problem, then solved using integer linear programming. Moreover, when available, acoustic speaker models can be added seamlessly to the graph structure for joint named and acoustic speaker identification – leading to a 10% error decrease (from 45% down to 35%) over a state-of-the-art i-vector speaker identification system on the REPERE TV broadcast corpus.
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...