  Recently, a fully supervised speaker diarization approach was proposed
(UIS-RNN) which models speakers using multiple instances of a parameter-sharing
recurrent neural network. In this paper we propose qualitative modifications to
the model that significantly improve the learning efficiency and the overall
diarization performance. In particular, we introduce a novel loss function, we
called Sample Mean Loss and we present a better modelling of the speaker turn
behaviour, by devising an analytical expression to compute the probability of a
new speaker joining the conversation. In addition, we demonstrate that our
model can be trained on fixed-length speech segments, removing the need for
speaker change information in inference. Using x-vectors as input features, we
evaluate our proposed approach on the multi-domain dataset employed in the
DIHARD II challenge: our online method improves with respect to the original
UIS-RNN and achieves similar performance to an offline agglomerative clustering
baseline using PLDA scoring.
