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This paper exploits the fact that when GMM and SVM classifiers with roughly the same level of performance exhibit uncorrelated errors they can be combined to produce a better classifier. The gain accrues from combining the descriptive strength of GMM models with the discriminative power of SVM classifiers. This idea, first exploited in the context of speaker recognition [1, 2] , is applied to speech recognition -specifically to a digit recognition task in a noisy environment -with significant gains in performance.doi:10.1109/icassp.2002.5743651 dblp:conf/icassp/FineSG02 fatcat:rz7rna733je37jjduevc6o2g44