2,918 research outputs found
Joint Bayesian Gaussian discriminant analysis for speaker verification
State-of-the-art i-vector based speaker verification relies on variants of
Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We
are mainly motivated by the recent work of the joint Bayesian (JB) method,
which is originally proposed for discriminant analysis in face verification. We
apply JB to speaker verification and make three contributions beyond the
original JB. 1) In contrast to the EM iterations with approximated statistics
in the original JB, the EM iterations with exact statistics are employed and
give better performance. 2) We propose to do simultaneous diagonalization (SD)
of the within-class and between-class covariance matrices to achieve efficient
testing, which has broader application scope than the SVD-based efficient
testing method in the original JB. 3) We scrutinize similarities and
differences between various Gaussian PLDAs and JB, complementing the previous
analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are
conducted on NIST SRE10 core condition 5, empirically validating the
superiority of JB with faster convergence rate and 9-13% EER reduction compared
with state-of-the-art PLDA.Comment: accepted by ICASSP201
Bayesian distance metric learning and its application in automatic speaker recognition systems
This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data
Anti-spoofing Methods for Automatic SpeakerVerification System
Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer
and Information Science (CCIS) vol. 66
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