This paper investigates the use of deep neural networks (DNNs) for the task of spoken language identification. Various feed-forward fully connected, convolutional and recurrent DNN architectures are adopted and compared against a baseline i-vector based system. Moreover, DNNs are also utilized for extraction of bottleneck features from the input signal. The dataset used for experimental evaluation contains utterances belonging to languages that are all related to each other and sometimes hard to distinguish even for human listeners: it is compiled from recordings of the 11 most widespread Slavic languages. We also released this Slavic dataset to the general public, because a similar collection is not publicly available through any other source. The best results were yielded by a bidirectional recurrent DNN with gated recurrent units that was fed by bottleneck features. In this case, the baseline ER was reduced from 4.2% to 1.2% and Cavg from 2.3% to 0.6%.

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