Generative ASR Post-Editing

A paradigm that uses large language models to post-process the N-best candidate hypotheses produced by automatic speech recognition (ASR). The language context is used to compare candidates and selectively rewrite the transcript in order to reduce word error rate (WER) without retraining the recognizer.