Unsupervised channel adaptation for language identification using co-training

Language identification (LID) of speech signals in conditions like adverse radio communication channel is a challenging problem. In this paper, we address the scenario of improving the performance of a LID system on mis-matched radio communication channels (not seen in training) given a small amount of speech data without language labels. We develop a co-training procedure using two diverse acoustic LID systems to improve the performance by effectively utilizing the adaptation data. The acoustic LID systems use different features, projection methods and back-end classifiers. Assuming that the classification errors for the diverse LID systems are independent, the co-training procedure improves the classification accuracy of each system. Various LID experiments are performed on the mis-matched channels in a leave-one-out setting for a variety of noise conditions. In these experiments, with small amounts of unsupervised data from the new channel, we show that the proposed co-training procedure provides significant improvement (average relative improvement of 32 %) over the baseline scenario of no-adaptation and noticeable improvements of about 10 % over a self-training framework.