  A deep learning approach has been proposed recently to derive speaker
identifies (d-vector) by a deep neural network (DNN). This approach has been
applied to text-dependent speaker recognition tasks and shows reasonable
performance gains when combined with the conventional i-vector approach.
Although promising, the existing d-vector implementation still can not compete
with the i-vector baseline. This paper presents two improvements for the deep
learning approach: a phonedependent DNN structure to normalize phone variation,
and a new scoring approach based on dynamic time warping (DTW). Experiments on
a text-dependent speaker recognition task demonstrated that the proposed
methods can provide considerable performance improvement over the existing
d-vector implementation.
