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ABSTRACTThis work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared to state-of-the-art i-vector based acoustic systems on two different datasets: Google 5M LID corpus and NIST LRE 2009. Results show how LID can largely benefit from using DNNs, especially when a large amount of training data is available. We found relative improvements up to 70%, in C avg , over the baseline system.
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification (LID). The use of RNNs is motivated by their better ability in modeling sequences with respect to feed forward networks used in previous works. We show that LSTM RNNs can effectively exploit temporal dependencies in acoustic data, learning relevant features for language discrimination purposes. The proposed approach is compared to baseline i-vector and feed forward Deep Neural Network (DNN) systems in the NIST Language Recognition Evaluation 2009 dataset. We show LSTM RNNs achieve better performance than our best DNN system with an order of magnitude fewer parameters. Further, the combination of the different systems leads to significant performance improvements (up to 28%).
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