The automatic speaker diarization consists in splitting the signal into homogeneous segments and clustering them by speakers. However the speaker segments are specified with anonymous labels. This pa- per proposed a solution to identify those speakers by extracting their full names pronounced in the show. With a semantic classification tree automatically built on a training corpus, the full names detected in transcription of a segment are associated to this segment or to one of its neighbors. Then, a merging method allows to associate a full name to a speaker cluster instead of a anonymous label provided by the diarization. The experiments are carried out over French broadcast news records from the ESTER 2005 evaluation campaign. About 70% show duration is correctly processed for both development and eval- uation corpora. On the evaluation corpus, 18.15% show duration is wrongly named and no decision is taken for 11.91% show duration.