Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition

Michael Alexander Nirschl
Dan Holtmann-Rice


Recurrent neural network language models (RNNLM) 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 a few of our own novel variants on a Youtube speech recognition task. Lattice rescoring using LSTMLMs reduces the word error rate (WER) for this task by about 6\% relative to the WER obtained using an N-gram LM.

Research Areas