Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation
Sreyan Ghosh; Mohammad Sadegh Rasooli; Michael Levit; Peidong Wang; Jian Xue; Dinesh Manocha; Jinyu Li · 2024 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
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 (D
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Metadata source: arXiv