Speaker verification in noisy environment using GMM supervectors

This paper explores the GMM-SVM combined approach for Text-Independent speaker verification in noisy environment. In recent years supervectors constructed by stacking the means of adapted Gaussian Mixture Models (GMMs) have been used successfully for deriving sequence kernels. Support Vector Machines (SVMs) trained using such kernels provide further improvement in classification accuracy. Analysis of the behavior of such hybrid systems towards simulated noisy data is the object of our study. In our work we have used the KL-divergence and GMM-UBM mean interval kernels for SVM training. All experiments are conducted on NIST-SRE-2003 database with training and test utterances degraded by noises (car, factory & pink) collected from the NOISEX-92 database, at 5dB & 10dB SNRs. A significant improvement of performance is observed in comparison to the traditional GMM-UBM based system.