Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification
Abstract
Frame alignments can be computed by different methods in GMM-based speaker verification. By incorporating a phonetic Gaussian mixture model (PGMM), we are able to compare the performance using alignments extracted from the deep neural networks (DNN) and the conventional hidden Markov model (HMM) in digit-prompted speaker verification. Based on the different characteristics of these two alignments, we present a novel content verification method to improve the system security without much computational overhead. Our experiments on the RSR2015 Part-3 digit-prompted task show that, the DNN based alignment performs on par with the HMM alignment. The results also demonstrate the effectiveness of the proposed Kullback-Leibler (KL) divergence based scoring to reject speech with incorrect pass-phrases.
Keywords
Cite
@article{arxiv.1710.10436,
title = {Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification},
author = {Yi Liu and Liang He and Weiqiang Zhang and Jia Liu and Michael T. Johnson},
journal= {arXiv preprint arXiv:1710.10436},
year = {2018}
}
Comments
accepted by APSIPA ASC 2018