  We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech
recognition, a novel method that combines neural network language models
(NNLMs) and finite state transducers (FSTs) in a mathematically consistent
framework. Our method utilizes a background NNLM which models generic
background text together with a collection of domain-specific entities modeled
as individual FSTs. Each output token is generated by a mixture of these
components; the mixture weights are estimated with a separately trained neural
decider. We show that NFCLM significantly outperforms NNLM by 15.8% relative in
terms of Word Error Rate. NFCLM achieves similar performance as traditional
NNLM and FST shallow fusion while being less prone to overbiasing and 12 times
more compact, making it more suitable for on-device usage.
