This paper presents a detailed description and analysis of a joint submission of Institute for Infocomm Research (I2R) and National University of Singapore (NUS), which is the top performing system to AP16-OL7 Challenge. The submitted system was a fusion of two sub-systems: the i-vector system and GMM-SVM system, both based on state-of-the-art bottleneck feature. Central to our work presented in this paper is a language-dependent UBM GMM-SVM system and traditional i-vector polynomials expansion with SVM classifier. The FoCal toolkit was used for sub-system fusion. Experimental results show that the proposed approach achieves significant improvement over the baseline system on the development and evaluation sets. Our final submission achieve EER 0.440%, 1.09% and identification rates 98.9%, 97.6% on the development set and evaluation set, respectively.