FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition

Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence, and can further reduce the word error rate (WER). Although multiple candidates are generated by an ASR system through beam search, current error correction approaches can only correct one sentence at a time, failing to leverage the voting effect1 from multiple candidates to better detect and correct error tokens. In this work, we propose FastCorrect 2, an error correction model that takes multiple ASR candidates as input for better correction accuracy.

2021: Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Linquan Liu, Tao Qin, Xiang-Yang Li, Ed Lin, Tie-Yan Liu

https://arxiv.org/pdf/2109.14420v3.pdf