Discriminative Features for Language Identification

Abstract

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 supervectors 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.

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@inproceedings{Alberti2011DiscriminativeFF, title={Discriminative Features for Language Identification}, author={Christopher Alberti and Michiel Bacchiani}, booktitle={INTERSPEECH}, year={2011} }