This paper presents a detailed description and analysis of I2R submission, which is among the top performing systems, to the 2015 NIST language recognition i-vector machine learning challenge. Our submission is a fusion of several sub-systems based on linear discriminant analysis (LDA), support vector machine (SVM), multi-layer perceptron (MLP), deep neural network (DNN), and multi-class logistic regression. Central to our work presented in this paper is a novel out-of-set (OOS) detection scheme for selecting i-vectors from an unlabeled development set. It consists of a best fit out-of-set selection followed by cluster purification. We also propose a novel empirical kernel map to be used with SVM. Experimental results show that the proposed approach achieves significant improvement on both the progress and evaluation sets defined for the i-vector challenge. Our final submission achieves 55.0% and 54.5% relative improvement over the baseline system on the progress and evaluation sets, respectively.
Cite as: Sun, H., Nguyen, T.H., Wang, G., Lee, K.A., Ma, B., Li, H. (2016) I2R Submission to the 2015 NIST Language Recognition I-vector Challenge. Proc. The Speaker and Language Recognition Workshop (Odyssey 2016), 311-318, doi: 10.21437/Odyssey.2016-45
@inproceedings{sun16_odyssey,
author={Hanwu Sun and Trung Hieu Nguyen and Guangsen Wang and Kong Aik Lee and Bin Ma and Haizhou Li},
title={{I2R Submission to the 2015 NIST Language Recognition I-vector Challenge}},
year=2016,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2016)},
pages={311--318},
doi={10.21437/Odyssey.2016-45}
}