@inproceedings{lin-etal-2026-pseudo2real,
    title = "{P}seudo2{R}eal: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition",
    author = "Lin, Yi-Cheng  and
      Liang, Yu-Hsuan Li  and
      Su, Hsuan  and
      Lin, Tzu-Quan  and
      Chen, Shang-Tse  and
      Chen, Yun-Nung  and
      Lee, Hung-yi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.59/",
    pages = "1160--1175",
    ISBN = "979-8-89176-395-1",
    abstract = "Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although pseudo-labeling offers a practical workaround, it often introduces systematic, accent-specific errors that filtering fails to fix. We ask: How can we correct these recurring biases without target ground truth? We propose a simple parameter-space correction: in a source domain containing both real and pseudo-labeled data, two ASR models are fine-tuned from the same initialization, one on ground-truth labels and the other on pseudo-labels, and their weight difference forms a correction vector that captures pseudo-label biases.When applied to a pseudo-labeled target model, this vector enhances recognition, achieving up to a 35{\%} relative Word Error Rate (WER) reduction on AfriSpeech-200 across ten African accents with the Whisper tiny model."
}