In this paper we investigate the use of discriminatively trained feature transforms to improve the accuracy of a MAP-SVM language recognition system. We train the feature transforms by alternatively solving an SVM optimization on MAP super-vectors estimated from transformed features, and performing a small step on the transforms in the direction of the antigradient of the SVM objective function. We applied this method on the LRE2003 dataset, and obtained an 5.9% relative reduction of pooled equal error rate.
Bibliographic reference. Alberti, Chris / Bacchiani, Michiel (2011): "Discriminative features for language identification", In INTERSPEECH-2011, 2917-2920.