Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition
Findings of the Association for Computational Linguistics: ACL 2026 , 1160–1175 , 2026
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.
BibTeX
@inproceedings{lin2025pseudoreal,
title = {Pseudo2Real: 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},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
year = {2026},
pages = {1160--1175},
doi = {10.18653/v1/2026.findings-acl.59},
}