  Recurrent neural network (RNN) language models (LMs) and Long Short Term
Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform
traditional N-gram LMs on speech recognition tasks. However, these models are
computationally more expensive than N-gram LMs for decoding, and thus,
challenging to integrate into speech recognizers. Recent research has proposed
the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an
efficient strategy to integrate these models into a speech recognition system.
In this paper, we evaluate existing lattice rescoring algorithms along with new
variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs
reduces the word error rate (WER) for this task by 8\% relative to the WER
obtained using an N-gram LM.
