This paper discusses a set of modifications regarding the use of the Bayesian Information Criterion (BIC) for the speaker diarization task. We focus on the specific variant of the BIC that deploys models of equal -or roughly equal- statistical complexity under partitions of different number of speakers and we examine three modifications. Firstly, we investigate a way to deal with the permutation-invariance property of the estimators when dealing with mixture models, while the second is derived by attaching a weakly informative prior over the space of speaker-level state sequences. Finally, based on the recently proposed segmental-BIC approach, we examine its effectiveness when mixture of gaussians are used to model the emission probabilities of a speaker. The experiments are carried out using NIST rich transcription evaluation campaign for meeting data and show improvement over the baseline setting.