Sample selection for automatic language identification

Current approaches to automatic spoken language identification (LID) assume the availability of a large corpus of manually language-labeled speech samples for training statistical classifiers. We investigate two methods of active learning to significantly reduce the amount of labeled speech needed for training LID systems. Starting with a small training set, an automated method is used to select samples from a corpus of unlabeled speech, which are then labeled and added to the training pool - one selection method is based on a previously known entropy criterion, and another on a novel likelihood-ratio criterion. We demonstrate LID performance comparable to a large training corpus using only a tenth of the training data. A further 40% improvement in LID performance is obtained using a third of the training data. Finally, we show that our novel selection method is more robust to variance in the unlabeled pool than the entropy based method.