  Speaker diarisation systems often cluster audio segments using speaker
embeddings such as i-vectors and d-vectors. Since different types of embeddings
are often complementary, this paper proposes a generic framework to improve
performance by combining them into a single embedding, referred to as a
c-vector. This combination uses a 2-dimensional (2D) self-attentive structure,
which extends the standard self-attentive layer by averaging not only across
time but also across different types of embeddings. Two types of 2D
self-attentive structure in this paper are the simultaneous combination and the
consecutive combination, adopting a single and multiple self-attentive layers
respectively. The penalty term in the original self-attentive layer which is
jointly minimised with the objective function to encourage diversity of
annotation vectors is also modified to obtain not only different local peaks
but also the overall trends in the multiple annotation vectors. Experiments on
the AMI meeting corpus show that our modified penalty term improves the d-
vector relative speaker error rate (SER) by 6% and 21% for d-vector systems,
and a 10% further relative SER reduction can be obtained using the c-vector
from our best 2D self-attentive structure.
