Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation
Year: 2025

Generative Error Correction (GEC) has emerged as a powerful post-processing method to boost the performance of Automatic Speech Recognition (ASR) systems.In this paper, we first 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 i