  In this paper we present a new method for text-independent speaker
verification that combines segmental dynamic time warping (SDTW) and the
d-vector approach. The d-vectors, generated from a feed forward deep neural
network trained to distinguish between speakers, are used as features to
perform alignment and hence calculate the overall distance between the
enrolment and test utterances.We present results on the NIST 2008 data set for
speaker verification where the proposed method outperforms the conventional
i-vector baseline with PLDA scores and outperforms d-vector approach with local
distances based on cosine and PLDA scores. Also score combination with the
i-vector/PLDA baseline leads to significant gains over both methods.
