LT-LM: a novel non-autoregressive language model for single-shot lattice\n rescoring
Journal: arXiv (Cornell University)
Year: 2021

Neural network-based language models are commonly used in rescoring\napproaches to improve the quality of modern automatic speech recognition (ASR)\nsystems. Most of the existing methods are computationally expensive since they\nuse autoregressive language models. We propose a novel rescoring approach,\nwhich processes the entire lattice in a single call to the model. The key\nfeature of our rescoring policy is a novel non-autoregressive Lattice\nTransformer Language Model (LT-LM). This model ta