Robust Speaker Verification Using Low-Rank Recovery under Total Variability Space

In this paper we propose an speaker verification approach by applying low-rank recovery approach under total variability space, which is trained by a modified Gaussian Mixture Modeling (MGMM) with the observation confidence. In this model, we construct UBM mean supervector by MGMM in order to train total variability matrix and obtain i-vectors. Besides, the low-rank recovery method is exploited to model i-vectors under the total variability space. Experiment results on utterances from Korean movie ("You came from the stars") show that our proposed approach can significantly enhance the performance of speaker verification and outperform the baseline GMM_UBM, GMM-supervector in noisy environments.