13th Annual Conference of the International Speech Communication Association

Portland, OR, USA
September 9-13, 2012

LSTM Neural Networks for Language Modeling

Martin Sundermeyer, Ralf Schlüter, Hermann Ney

Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, Aachen, Germany

Neural networks have become increasingly popular for the task of language modeling. Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks can take into account all of the predecessor words. On the other hand, it is well known that recurrent networks are difficult to train and therefore are unlikely to show the full potential of recurrent models.
   These problems are addressed by a the Long Short-Term Memory neural network architecture. In this work, we apply this type of network to an English and a large French language modeling task. Experiments show improvements of about 8% relative in perplexity over standard recurrent neural network LMs. In addition, we gain considerable improvements in WER on top of a state-of-the-art speech recognition system.

Index Terms: language modeling, recurrent neural networks, LSTM neural networks

Full Paper

Bibliographic reference.  Sundermeyer, Martin / Schlüter, Ralf / Ney, Hermann (2012): "LSTM neural networks for language modeling", In INTERSPEECH-2012, 194-197.