  Speaker diarization, or the task of finding "who spoke and when", is now used
in almost every speech processing application. Nevertheless, its fairness has
not yet been evaluated because there was no protocol to study its biases one by
one. In this paper we propose a protocol and a scoring method designed to
evaluate speaker diarization fairness. This protocol is applied on a large
dataset of spoken utterances and report the performances of speaker diarization
depending on the gender, the age, the accent of the speaker and the length of
the spoken sentence. Some biases induced by the gender, or the accent of the
speaker were identified when we applied a state-of-the-art speaker diarization
method.
