Deep Nonlinear Metric Learning for Speaker Verification in the I-Vector Space
نویسندگان
چکیده
منابع مشابه
Deep Nonlinear Metric Learning for Speaker Verification in the I-Vector Space
Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learnin...
متن کاملBayesian distance metric learning on i-vector for speaker verification
This thesis explores the use of Bayesian distance metric learning (Bayes dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an approximation of the distance metric as a weighted covariance matrix of the top eigenvectors from th...
متن کاملJoint Speaker Verification and Antispoofing in the i-Vector Space
Any biometric recognizer is vulnerable to spoofing attacks and hence voice biometric, also called automatic speaker verification (ASV), is no exception; replay, synthesis and conversion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks considered as one of the most challenging for modern recognition systems. To detect spoofing, most...
متن کاملMaximum Likelihood i-vector Space Using PCA for Speaker Verification
This paper proposes a new approach to training the i-vector space using a variant of PCA with the Baum-Welch statistics for speaker verification. In eigenvoice the rank of variability space is bounded by the number of training speakers, so a variant of the probabilistic PCA approach is introduced for estimating the parameters. But this constraint doesn’t exist in i-vector model because the numb...
متن کاملAn i-vector backend for speaker verification
We propose a new approach to the problem of uncertainty modeling in text-dependent speaker verification where speaker factors are used as the feature representation. The state-of-the-art backend in this situation consists in using point estimates of speaker factors to model the joint distribution of pairs of enrollment and test feature vectors under the same-speaker hypothesis. We develop a ver...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2017
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2016edl8106