Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
Abstract
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.
Cite
@article{arxiv.1506.08349,
title = {Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition},
author = {Lantian Li and Yiye Lin and Zhiyong Zhang and Dong Wang},
journal= {arXiv preprint arXiv:1506.08349},
year = {2015}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1505.06427