Enhancing GMM scores using SVM hints

This paper proposes a classification scheme that combines statistical models and support vector machines. It exploits the fact (observed in [1]) that GMM and SVM classifiers with roughly the same level of performance produce uncorrelated errors. We describe a novel scheme which employs an SVM classifier as an "advisor" to the GMM classifier in uncertain cases. The utility of the combined generative/discriminative approach is demonstrated on standard text-independent speaker verification and speaker identification tasks in matched and mismatched training and test conditions. Results indicate significant improvements in performance without much computational overhead.