Test:2010 RecurrentNeuralNetworkbasedLang

• (Mikolov et al., 2010) ⇒ Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan Cernocky, and Sanjeev Khudanpur. (2010). “Recurrent Neural Network based Language Model.” In: Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH 2010).

Subject Headings: Recurrent Neural Network; Language Model; Recurrent Neural Network Language Model; Sequential Data Prediction

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Recurrent+Neural+Network+based+Language+Model

Quotes

Author Keywords

• Language modeling; recurrent neural networks; speech recognition

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 RecurrentNeuralNetworkbasedLang	Lukas Burget Tomáš Mikolov Sanjeev Khudanpur Jan Cernocky Martin Karafiat			Recurrent Neural Network based Language Model