Deep Speaker Vectors for Semi Text-independent Speaker Verification
Abstract
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi text-independent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.
Keywords
Cite
@article{arxiv.1505.06427,
title = {Deep Speaker Vectors for Semi Text-independent Speaker Verification},
author = {Lantian Li and Dong Wang and Zhiyong Zhang and Thomas Fang Zheng},
journal= {arXiv preprint arXiv:1505.06427},
year = {2015}
}