  Speaker verification (SV) systems using deep neural network embeddings,
so-called the x-vector systems, are becoming popular due to its good
performance superior to the i-vector systems. The fusion of these systems
provides improved performance benefiting both from the discriminatively trained
x-vectors and generative i-vectors capturing distinct speaker characteristics.
In this paper, we propose a novel method to include the complementary
information of i-vector and x-vector, that is called generative x-vector. The
generative x-vector utilizes a transformation model learned from the i-vector
and x-vector representations of the background data. Canonical correlation
analysis is applied to derive this transformation model, which is later used to
transform the standard x-vectors of the enrollment and test segments to the
corresponding generative x-vectors. The SV experiments performed on the NIST
SRE 2010 dataset demonstrate that the system using generative x-vectors
provides considerably better performance than the baseline i-vector and
x-vector systems. Furthermore, the generative x-vectors outperform the fusion
of i-vector and x-vector systems for long-duration utterances, while yielding
comparable results for short-duration utterances.
