  Speaker diarization, usually denoted as the ''who spoke when'' task, turns
out to be particularly challenging when applied to fictional films, where many
characters talk in various acoustic conditions (background music, sound
effects...). Despite this acoustic variability , such movies exhibit specific
visual patterns in the dialogue scenes. In this paper, we introduce a two-step
method to achieve speaker diarization in TV series: a speaker diarization is
first performed locally in the scenes detected as dialogues; then, the
hypothesized local speakers are merged in a second agglomerative clustering
process, with the constraint that speakers locally hypothesized to be distinct
must not be assigned to the same cluster. The performances of our approach are
compared to those obtained by standard speaker diarization tools applied to the
same data.
