The Intelligent Voice 2016 Speaker Recognition System

We trained on each acoustic feature a full covariance, genderindependent UBM model with 2048 Gaussians followed by a 600-dimensional i-vector extractor to establish our MFCCand PLP-based i-vector systems. The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor. The open-source Kaldi software has been used for all these processing steps [20]. It has been shown that successive acoustic observation vectors tend to be highly correlated. This may be problematic for maximum a posteriori (MAP) estimation of i-vectors. To investigating this issue, scaling the zero and first order Baum-Welch statistics before presenting them to the i-vector extractor has been proposed. It turns out that a scale factor of 0.33 gives a slight edge, resulting in a better decision cost function [10]. This scaling factor has been performed in training the i-vector extractor as well as in the testing.