AI summary

This paper analyzes Long Short-Term Memory (LSTM) neural networks for language modeling on English and French tasks, demonstrating an 8% relative improvement in perplexity over standard recurrent neural networks and considerable word error rate improvements on a state-of-the-art speech recognition system.

Abstract

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 analyze this type of network on 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.