title: SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition
abstract: Error correction in automatic speech recognition (ASR) aims to correct those incorrect words in sentences generated by ASR models. Since recent ASR models usually have low word error rate (WER), to avoid affecting originally correct tokens, error correction models should only modify incorrect words, and therefore detecting incorrect words is important for error correction. Previous works on error correction either implicitly detect error words through target-source attention or CTC (connectionis
journal: Proceedings of the AAAI Conference on Artificial Intelligence