  In this study we address the problem of training a neuralnetwork for language
identification using both labeled and unlabeled speech samples in the form of
i-vectors. We propose a neural network architecture that can also handle
out-of-set languages. We utilize a modified version of the recently proposed
Ladder Network semisupervised training procedure that optimizes the
reconstruction costs of a stack of denoising autoencoders. We show that this
approach can be successfully applied to the case where the training dataset is
composed of both labeled and unlabeled acoustic data. The results show enhanced
language identification on the NIST 2015 language identification dataset.
