@inproceedings{mathur-etal-2024-doc,
    title = "{DOC}-{RAG}: {ASR} Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation",
    author = "Mathur, Puneet  and
      Liu, Zhe  and
      Li, Ke  and
      Ma, Yingyi  and
      Karen, Gil  and
      Ahmed, Zeeshan  and
      Manocha, Dinesh  and
      Zhang, Xuedong",
    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.457",
    pages = "5132--5139",
    abstract = "We propose DOC-RAG - Domain-distributed Co-occurrence Retrieval Augmentation for ASR language model personalization aiming to improve the automatic speech recognition of rare word patterns in unseen domains. Our approach involves contrastively training a document retrieval module to rank external knowledge domains based on their semantic similarity with respect to the input query. We further use n-gram co-occurrence distribution to recognize rare word patterns associated with specific domains. We aggregate the next word probability distribution based on the relative importance of different domains. Extensive experiments on three user-specific speech-to-text tasks for meetings, TED talks, and financial earnings calls show that DOC-RAG significantly outperforms strong baselines with an 8-15{\%} improvement in terms of perplexity and a 4-7{\%} reduction in terms of Word Error Rates in various settings.",
}

We propose DOC-RAG - Domain-distributed Co-occurrence Retrieval Augmentation for ASR language model personalization aiming to improve the automatic speech recognition of rare word patterns in unseen domains. Our approach involves contrastively training a document retrieval module to rank external knowledge domains based on their semantic similarity with respect to the input query. We further use n-gram co-occurrence distribution to recognize rare word patterns associated with specific domains. We aggregate the next word probability distribution based on the relative importance of different domains. Extensive experiments on three user-specific speech-to-text tasks for meetings, TED talks, and financial earnings calls show that DOC-RAG significantly outperforms strong baselines with an 8-15{\%} improvement in terms of perplexity and a 4-7{\%} reduction in terms of Word Error Rates in various settings.
Mathur, Puneet Liu, Zhe Li, Ke Ma, Yingyi Karen, Gil Ahmed, Zeeshan Manocha, Dinesh Zhang, Xuedong
DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation