  The performance of speaker diarization is strongly affected by its clustering
algorithm at the test stage. However, it is known that clustering algorithms
are sensitive to random noises and small variations, particularly when the
clustering algorithms themselves suffer some weaknesses, such as bad local
minima and prior assumptions. To deal with the problem, a compact
representation of speech segments with small within-class variances and large
between-class distances is usually needed. In this paper, we apply an
unsupervised deep model, named multilayer bootstrap network (MBN), to further
process the embedding vectors of speech segments for the above problem. MBN is
an unsupervised deep model for nonlinear dimensionality reduction. Unlike
traditional neural network based deep model, it is a stack of $k$-centroids
clustering ensembles, each of which is trained simply by random resampling of
data and one-nearest-neighbor optimization. We construct speaker diarization
systems by combining MBN with either the i-vector frontend or x-vector
frontend, and evaluated their effectiveness on a simulated NIST diarization
dataset, the AMI meeting corpus, and NIST SRE 2000 CALLHOME database.
Experimental results show that the proposed systems are better than or at least
comparable to the systems that do not use MBN.
