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

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

Sound · Submitted 28 Oct 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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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arXiv ID1710.10436 (version 4)
Pith IDRJTMPYST, Pith's permanent record of this paper and its review
CitationJia Liu, Liang He, Michael T. Johnson, Weiqiang Zhang, Yi Liu. "Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification." arXiv:1710.10436 (2017). https://pith.science/paper/RJTMPYST
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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.17 · human participants 0.37 · controlled comparison 0.81 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.91 · mentions code or data availability 0.02.

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arXiv listing abstract v4. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Frame alignments can be computed by different methods in GMM-based speaker verification. background 1.00 0.06 0.02 asserted 0.85
s1 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. method 0.74 0.87 0.06 asserted 0.86
s2 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. contribution 0.99 0.85 0.20 asserted 0.98
s3 Our experiments on the RSR2015 Part-3 digit-prompted task show that, the DNN based alignment performs on par with the HMM alignment. result 1.00 0.96 0.28 asserted 1.00
s4 The results also demonstrate the effectiveness of the proposed Kullback-Leibler (KL) divergence based scoring to reject speech with incorrect pass-phrases. result 0.98 0.94 0.30 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.94 · evidence from observation or experiment 0.98.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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