Discriminative features for language identification
Chris Alberti, Michiel Bacchiani · 2011
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 antigradi-ent 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. Index Terms — Language recognition, support vector machines, discriminative feature transforms.