  Due to the mismatch of statistical distributions of acoustic speech between
training and testing sets, the performance of spoken language identification
(SLID) could be drastically degraded. In this paper, we propose an unsupervised
neural adaptation model to deal with the distribution mismatch problem for
SLID. In our model, we explicitly formulate the adaptation as to reduce the
distribution discrepancy on both feature and classifier for training and
testing data sets. Moreover, inspired by the strong power of the optimal
transport (OT) to measure distribution discrepancy, a Wasserstein distance
metric is designed in the adaptation loss. By minimizing the classification
loss on the training data set with the adaptation loss on both training and
testing data sets, the statistical distribution difference between training and
testing domains is reduced. We carried out SLID experiments on the oriental
language recognition (OLR) challenge data corpus where the training and testing
data sets were collected from different conditions. Our results showed that
significant improvements were achieved on the cross domain test tasks.
