This paper describes the systems developed by the Software Technologies Working Group of the University of the Basque Country (EHU) for the NIST 2011 Language Recognition Evaluation (LRE). One primary and three contrastive systems were submitted, all of them fusing five component subsystems: a Linearized Eigenchannel GMM (LE-GMM) subsystem, an iVector subsystem and three phone-lattice-SVM subsystems based on the publicly available BUT decoders for Czech, Hungarian an Russian. The four submitted systems were identical except for the backend approach and the development dataset used to estimate the backend and fusion parameters. Multiclass discriminative fusion was performed separately for each nominal duration. A development set was defined, including the evaluation sets of LRE07 and LRE09 and the development data provided by NIST for 9 additional languages in the 2011 campaign. The official results, which were among the best submitted to the evaluation, are presented and briefly discussed. Post-key analyses are also addressed in the paper, including the performance attained by component subsystems and a study of their contribution to fusion performance by means of a greedy selection procedure.

Index Terms: Spoken Language Recognition, NIST 2011 LRE, Gaussian Backend, Multiclass Discriminative Fusion