@inproceedings{jiang-etal-2024-contextual,
    title = "Contextual Modeling for Document-level {ASR} Error Correction",
    author = "Jiang, Jin  and
      Yin, Xunjian  and
      Wan, Xiaojun  and
      Peng, Wei  and
      Li, Rongjun  and
      Yang, Jingyuan  and
      Zhou, Yanquan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.341",
    pages = "3855--3867",
    abstract = "Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a $k$-Nearest Neighbors ($k$NN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.",
}

Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a $k$-Nearest Neighbors ($k$NN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.
Jiang, Jin Yin, Xunjian Wan, Xiaojun Peng, Wei Li, Rongjun Yang, Jingyuan Zhou, Yanquan
Contextual Modeling for Document-level ASR Error Correction