Language recognition with support vector machines and shifted-delta cepstral features has been an excellent performer in NIST-sponsored language evaluation for many years. A novel improvement of this method has been the introduction of hybrid SVM/GMM systems. These systems use GMM supervectors as an SVM expansion for classification. In prior work, methods for scoring SVM/GMM systems have been introduced based upon either standard SVM scoring or GMM scoring with a pushed model. Although prior work showed experimentally that GMM scoring yielded better results, no framework was available to explain the connection between SVM scoring and GMM scoring. In this paper, we show that there are interesting connections between SVM scoring and GMM scoring. We provide a framework both theoretically and experimentally that connects the two scoring techniques. This connection should provide the basis for further research in SVM discriminative training for GMM models.