@inproceedings{kurian-etal-2025-domain,
    title = "Domain-Specific Adaptation for {ASR} through Text-Only Fine-Tuning",
    author = "Kurian, Betty  and
      Upadhyay, Abhinav  and
      Sengupta, Abhijeet",
    editor = "Shukla, Ankita  and
      Kumar, Sandeep  and
      Bedi, Amrit Singh  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 1st Workshop on Multimodal Models for Low-Resource Contexts and Social Impact (MMLoSo 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.mmloso-1.7/",
    pages = "78--85",
    ISBN = "979-8-89176-311-1",
    abstract = "Speech recognition models often struggle in specialized domains due to the lack of domain-specific paired audio-text data, making it difficult to adapt general-purpose systems to unique terminology and linguistic patterns. In this work, we propose a text-only domain adaptation method for Whisper, fine-tuning only the decoder using domain-relevant text. Our approach introduces trainable cross-attention bias embeddings, extended with a gated mixture-of-experts routing mechanism, enabling the model to encode domain-specific linguistic priors without any audio data. Unlike ASR adaptation methods that require paired audio-text datasets, our approach is lightweight and resource-efficient. We observe up to a 56{\%} relative improvement in word error rate over the baseline. Our findings demonstrate that text-only adaptation is a practical and effective strategy for improving speech recognition in specialized domains with limited or no domain-specific audio."
}