4 accepted papers
In this study, we present systems submitted by the Center for Robust Speech Systems (CRSS) from UTDallas to NIST SRE 2018 (SRE18). Three alternative front-end speaker embedding frameworks are investigated, that includes: (i) i-vector, (ii) x-vector, (iii) and a modified triplet speaker embedding sys…
Sustaining automatic speaker verification(ASV) systems from spoofing attacks remains an essential challenge, even if significant progress in ASV has been achieved in recent years. In this study, an automatic spoofing detection approach using an i-vector framework is proposed. Two approaches are used…
This study proposes a novel deep neural network (DNN) based approach to language identification (LID) for the NIST 2015 Language Recognition (LRE) i-Vector Machine Learning Challenge. State-of-the-art DNN based LID systems utilize large amounts of labeled training data. The 2015 LRE i-Vector Machine…
In this paper, we present the system developed by the Center for Robust Speech Systems (CRSS), University of Texas at Dallas, for the NIST 2015 language recognition i-vector machine learning challenge. Our system includes several subsystems, based on Linear Discriminant Analysis - Support Vector Mac…