BAT System Description for NIST LRE 2015

In this paper, we summarize our efforts in the NIST Language Recognition (LRE) 2015 Evaluations which resulted in systems providing very competitive performance. We provide both the descriptions and the analysis of the systems that weincluded in our submission. We start by detailed description of the datasets that we used for training and development, and we follow by describing the models and methods that were used to produce the final scores. These include the front-end (i.e., the voice activity detection and feature extraction), the back-end (i.e., the final classifier), and the calibration and fusion stages. Apart from the techniques commonly used in the field (such as i-vectors, DNN bottle-Neck features, NN classifiers, Gaussian Back-ends, etc.), we present less-common methods, such as Sequence Summarizing Neural Networks (SSNN), and Automatic Unit Discovery. We present the performance of the systems both on the Fixed condition (where participants are required to use predefined data sets only), and the Open condition (where participants are allowed to use any publicly available resource) of the NIST LRE 2015.