  Speaker diarization determines who spoke and when? in an audio stream. In
this study, we propose a model-based approach for robust speaker clustering
using i-vectors. The ivectors extracted from different segments of same speaker
are correlated. We model this correlation with a Markov Random Field (MRF)
network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse
Covariance (TIC) matrix to represent the MRF correlation network for each
speaker. This approaches captures the sequential structure of i-vectors (or
equivalent speaker turns) belonging to same speaker in an audio stream. A
variant of standard Expectation Maximization (EM) algorithm is adopted for
deriving closed-form solution using dynamic programming (DP) and the
alternating direction method of multiplier (ADMM). Our diarization system has
four steps: (1) ground-truth segmentation; (2) i-vector extraction; (3)
post-processing (mean subtraction, principal component analysis, and
length-normalization) ; and (4) proposed speaker clustering. We employ cosine
K-means and movMF speaker clustering as baseline approaches. Our evaluation
data is derived from: (i) CRSS-PLTL corpus, and (ii) two meetings subset of the
AMI corpus. Relative reduction in diarization error rate (DER) for CRSS-PLTL
corpus is 43.22% using the proposed advancements as compared to baseline. For
AMI meetings IS1000a and IS1003b, relative DER reduction is 29.37% and 9.21%,
respectively.
