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arXiv · 1710.10436

Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification

Yi Liu·Liang He·Weiqiang Zhang·Jia Liu·Michael T. Johnson
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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.

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Yi Liu, Liang He, Weiqiang Zhang, Jia Liu, Michael T. Johnson. 2017-10-28. Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification. https://arxiv.org/abs/1710.10436

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