Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic
  Speech Recognition
Authors: name: Yi-Cheng Lin, name: Yu-Hsuan Li Liang, name: Hsuan Su, name: Tzu-Quan Lin, name: Shang-Tse Chen, name: Yun-Nung Chen, name: Hung-yi Lee

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.