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.

Stats

• citations3
• S2 citations—
• github stars0
• HF likes0
• heat score4.52
• arxiv keyliu2017investigation

Related papers

• Gmm-resnext: Combining Generative And Discriminative Models For Speaker Verification (2024)4.52
• Comparison Of Multiple Features And Modeling Methods For Text-dependent Speaker Verification (2017)0.00
• Adversarial Attacks On GMM I-vector Based Speaker Verification Systems (2019)13.65
• Deep Neural Network Based I-vector Mapping For Speaker Verification Using Short Utterances (2018)0.00
• Phonetic-attention Scoring For Deep Speaker Features In Speaker Verification (2018)2.26
• Parameterized Channel Normalization For Far-field Deep Speaker Verification (2021)3.58
• Harmonic-aligned Frame Mask Based On Non-stationary Gabor Transform With Application To Content-dependent Speaker Comparison (2019)2.26
• Linear Regression For Speaker Verification (2018)0.00