Failing Forward: Improving Generative Error Correction for ASR with
  Synthetic Data and Retrieval Augmentation
Authors: name: Sreyan Ghosh, name: Mohammad Sadegh Rasooli, name: Michael Levit, name: Peidong Wang, name: Jian Xue, name: Dinesh Manocha, name: Jinyu Li

Generative Error Correction (GEC) has emerged as a powerful post-processing
method to enhance the performance of Automatic Speech Recognition (ASR)
systems. However, we show that GEC models struggle to generalize beyond the
specific types of errors encountered during training, limiting their ability to
correct new, unseen errors at test time, particularly in out-of-domain (OOD)
scenarios. This phenomenon amplifies with named entities (NEs), where, in
addition to insufficient contextual information or knowledge about the NEs,
novel NEs keep emerging. To address these issues, we propose DARAG (Data- and
Retrieval-Augmented Generative Error Correction), a novel approach designed to
improve GEC for ASR in in-domain (ID) and OOD scenarios. We augment the GEC
training dataset with synthetic data generated by prompting LLMs and
text-to-speech models, thereby simulating additional errors from which the
model can learn. For OOD scenarios, we simulate test-time errors from new
domains similarly and in an unsupervised fashion. Additionally, to better
handle named entities, we introduce retrieval-augmented correction by
augmenting the input with entities retrieved from a database. Our approach is
simple, scalable, and both domain- and language-agnostic. We experiment on
multiple datasets and settings, showing that DARAG outperforms all our
baselines, achieving 8\% -- 30\% relative WER improvements in ID and 10\% --
33\% improvements in OOD settings.