An active learning method for speaker identity annotation in audio recordings

Given that manual annotation of speech is an expensive and long process, we attempt in this paper to assist an anno-tator to perform a speaker diarization. This assistance takes place in an annotation background for a large amount of archives. We propose a method which decreases the intervention number of a human. This method corrects a diarization by taking into account the human interventions. The experiment is done using French broadcast TV shows drawn from ANR-REPERE evaluation campaign. Our method is mainly evaluated in terms of KSR (Keystroke Saving Rate), and we reduce the number of actions needed to correct a speaker diarization output by 6.8% in absolute value.