UTD-CRSS system for the NIST 2015 language recognition i-vector machine learning challenge

Abstract

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 Machine (LDA-SVM) and deep neural network (DNN) approaches. An important feature of this challenge is the emphasis on out-of-set language detection. As a result, our system development focuses mainly on the evaluation and comparison of two different out-of-set language detection strategies: direct out-of-set detection and indirect out-of-set detection. These out-of-set detection strategies differ mainly on whether the unlabeled development data are used or not. The experimental results indicate that indirect out-of-set detection strategies used in our system could efficiently exploit the unlabeled development data, and therefore consistently outperform the direct out-of-set detection approach. Finally, by fusing four variants of indirect out-of-set detection based subsystems, our system achieves a relative performance gain of up to 45%, compared to the baseline cosine distance scoring (CDS) system provided by organizer.