This paper presents a Speech Technology Center (STC) system submitted to the NIST i-vector Challenge. The system includes different subsystems based on PLDA, LDA-SVM, RBM-PLDA and DBN-PLDA. We propose an original iterative scheme for clustering the NIST i-vector Challenge devset. We also introduce the RBM-PLDA subsystem in the NIST i-vector Challenge. Experiments performed on the progress dataset demonstrate that although the RBM-PLDA and DBN-PLDA subsystems are inferior to the other subsystems in terms of absolute minDCF, in the fusion they provide a substantial gain into the efficiency of the resulting STC system, reaching 0.239 at the minDCF point
This paper presents the QUT speaker recognition system, as a competing system in the Speakers In The...
This paper describes STBU 2006 speaker recognition system, which performed well in the NIST 2006 spe...
This PhD research developed new approaches to address speaker recognition system development using l...
The process of manually labeling data is very expensive and sometimes infeasible due to privacy and ...
In the paper recent methods used in the task of speaker recognition are presented. At first, the ext...
During late-2013 through mid-2014 NIST coordinated a special machine learning challenge based on the...
Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers...
i-vector modeling techniques have been successfully used for speaker clustering task recently. In th...
i-vector modeling techniques have been successfully used for speaker clustering task recently. In th...
In the paper recent methods used in the task of speaker recognition are presented. At first, the ext...
This paper briefly describes the speaker recognition systems developed by the Software Technolog
In this paper, we present a framework for unsupervised domain adap-tation of PLDA based i-vector spe...
This paper describes and discusses the "STBU" speaker recognition system, which performed well in th...
Previous studies have demonstrated the benefits of PLDA-SVM scoring with empirical kernel maps for i...
This paper presents the QUT speaker recognition system, as a competing system in the Speakers In The...
This paper presents the QUT speaker recognition system, as a competing system in the Speakers In The...
This paper describes STBU 2006 speaker recognition system, which performed well in the NIST 2006 spe...
This PhD research developed new approaches to address speaker recognition system development using l...
The process of manually labeling data is very expensive and sometimes infeasible due to privacy and ...
In the paper recent methods used in the task of speaker recognition are presented. At first, the ext...
During late-2013 through mid-2014 NIST coordinated a special machine learning challenge based on the...
Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers...
i-vector modeling techniques have been successfully used for speaker clustering task recently. In th...
i-vector modeling techniques have been successfully used for speaker clustering task recently. In th...
In the paper recent methods used in the task of speaker recognition are presented. At first, the ext...
This paper briefly describes the speaker recognition systems developed by the Software Technolog
In this paper, we present a framework for unsupervised domain adap-tation of PLDA based i-vector spe...
This paper describes and discusses the "STBU" speaker recognition system, which performed well in th...
Previous studies have demonstrated the benefits of PLDA-SVM scoring with empirical kernel maps for i...
This paper presents the QUT speaker recognition system, as a competing system in the Speakers In The...
This paper presents the QUT speaker recognition system, as a competing system in the Speakers In The...
This paper describes STBU 2006 speaker recognition system, which performed well in the NIST 2006 spe...
This PhD research developed new approaches to address speaker recognition system development using l...