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.
@article{arxiv.2303.00456,
title = {N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space},
author = {Rao Ma and Mark J. F. Gales and Kate M. Knill and Mengjie Qian},
journal= {arXiv preprint arXiv:2303.00456},
year = {2023}
}