A novel scores fusion approach applied on speaker verification under noisy environments

To improve the speaker verification system in adverse conditions, a novel score fusion approach using adaptive method, based on a prior Equal Error Rate (EER), is presented in this paper. Currently, the most commonly used methods are the mean, product, minimum, maximum, or the weighted sum of scores. Our method introduces the MLP network which approximates the estimated scores under noisy conditions, to those of the ideal estimated in clean environments and gives the optimally weighted parameters, to be added in the adaptive weights used for weighting sum of scores. This method is assessed by using the NIST 2000 corpus and different feature extraction methods. Noisy conditions are created using NOISEX-92. In severely degraded conditions, the results show that the speaker verification process using our proposed score fusion approach applied to the GMM-UBM and GMM-SVM based systems, achieves better performances in terms of EER reduction than each system used alone.