  Speaker Diarization (i.e. determining who spoke and when?) for multi-speaker
naturalistic interactions such as Peer-Led Team Learning (PLTL) sessions is a
challenging task. In this study, we propose robust speaker clustering based on
mixture of multivariate von Mises-Fisher distributions. Our diarization
pipeline has two stages: (i) ground-truth segmentation; (ii) proposed speaker
clustering. The ground-truth speech activity information is used for extracting
i-Vectors from each speechsegment. We post-process the i-Vectors with principal
component analysis for dimension reduction followed by lengthnormalization.
Normalized i-Vectors are high-dimensional unit vectors possessing
discriminative directional characteristics. We model the normalized i-Vectors
with a mixture model consisting of multivariate von Mises-Fisher distributions.
K-means clustering with cosine distance is chosen as baseline approach. The
evaluation data is derived from: (i) CRSS-PLTL corpus; and (ii) three-meetings
subset of AMI corpus. The CRSSPLTL data contain audio recordings of PLTL
sessions which is student-led STEM education paradigm. Proposed approach is
consistently better than baseline leading to upto 44.48% and 53.68% relative
improvements for PLTL and AMI corpus, respectively. Index Terms: Speaker
clustering, von Mises-Fisher distribution, Peer-led team learning, i-Vector,
Naturalistic Audio.
