An active learning method for speaker identity annotation in audio recordings

Abstract : 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.