On the use of deep feedforward neural networks for automatic language identification

•This work presents a comprehensive study on the use of deep neural networks for automatic language identification.•It includes a detailed performance analysis for different data selection strategies and DNN architectures.•Proposed systems are tested on the NIST Language Recognition Evaluation 2009, against an state-of-the-art i-vector baseline.•It also presents a novel approach that combines DNN and i-vector systems by using bottleneck features.•The combination of i-vector and bottleneck systems outperforms our baseline system by 45% in EER and Cavg, on 3s and 10s.