  In diarization, the PLDA is typically used to model an inference structure
which assumes the variation in speech segments be induced by various speakers.
The speaker variation is then learned from the training data. However, human
perception can differentiate speakers by age, gender, among other
characteristics. In this paper, we investigate a speaker-type informed model
that explicitly captures the known variation of speakers. We explore a mixture
of three PLDA models, where each model represents an adult female, male, or
child category. The weighting of each model is decided by the prior probability
of its respective class, which we study. The evaluation is performed on a
subset of the BabyTrain corpus. We examine the expected performance gain using
the oracle speaker type labels, which yields an 11.7% DER reduction. We
introduce a novel baby vocalization augmentation technique and then compare the
mixture model to the single model. Our experimental result shows an effective
0.9% DER reduction obtained by adding vocalizations. We discover empirically
that a balanced dataset is important to train the mixture PLDA model, which
outperforms the single PLDA by 1.3% using the same training data and achieving
a 35.8% DER. The same setup improves over a standard baseline by 2.8% DER.
