  Error correction models form an important part of Automatic Speech
Recognition (ASR) post-processing to improve the readability and quality of
transcriptions. Most prior works use the 1-best ASR hypothesis as input and
therefore can only perform correction by leveraging the context within one
sentence. In this work, we propose a novel N-best T5 model for this task, which
is fine-tuned from a T5 model and utilizes ASR N-best lists as model input. By
transferring knowledge from the pre-trained language model and obtaining richer
information from the ASR decoding space, the proposed approach outperforms a
strong Conformer-Transducer baseline. Another issue with standard error
correction is that the generation process is not well-guided. To address this a
constrained decoding process, either based on the N-best list or an ASR
lattice, is used which allows additional information to be propagated.
