11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Recurrent Neural Network Based Language Model

Tomáš Mikolov (1), Martin Karafiát (1), Lukáš Burget (1), Jan Černocký (1), Sanjeev Khudanpur (2)

(1) Brno University of Technology, Czech Republic
(2) Johns Hopkins University, USA

A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity.

Full Paper

Bibliographic reference.  Mikolov, Tomáš / Karafiát, Martin / Burget, Lukáš / Černocký, Jan / Khudanpur, Sanjeev (2010): "Recurrent neural network based language model", In INTERSPEECH-2010, 1045-1048.