  Speaker clustering is the task of forming speaker-specific groups based on a
set of utterances. In this paper, we address this task by using Dominant Sets
(DS). DS is a graph-based clustering algorithm with interesting properties that
fits well to our problem and has never been applied before to speaker
clustering. We report on a comprehensive set of experiments on the TIMIT
dataset against standard clustering techniques and specific speaker clustering
methods. Moreover, we compare performances under different features by using
ones learned via deep neural network directly on TIMIT and other ones extracted
from a pre-trained VGGVox net. To asses the stability, we perform a sensitivity
analysis on the free parameters of our method, showing that performance is
stable under parameter changes. The extensive experimentation carried out
confirms the validity of the proposed method, reporting state-of-the-art
results under three different standard metrics. We also report reference
baseline results for speaker clustering on the entire TIMIT dataset for the
first time.
