We propose a neural language modeling system based on low-rank adaptation
(LoRA) for speech recognition output rescoring. Although pretrained language
models (LMs) like BERT have shown superior performance in second-pass
rescoring, the high computational cost of scaling up the pretraining stage and
adapting the pretrained models to specific domains limit their practical use in
rescoring. Here we present a method based on low-rank decomposition to train a
rescoring BERT model and adapt it to new domains using only a fraction (0.08%)
of the pretrained parameters. These inserted matrices are optimized through a
discriminative training objective along with a correlation-based regularization
loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is
evaluated on LibriSpeech and internal datasets with decreased training times by
factors between 5.4 and 3.6.Comment: Accepted to IEEE ASRU 2023. Internal Review Approved. Revised 2nd
version with Andreas and Huck. The first version is in Sep 29th. 8 page