Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic
  Speech Recognition
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.
68ec5661cd07fb414898c8b7
Yi-Cheng Lin
68ec5661cd07fb414898c8b8
Yu-Hsuan Li Liang
68ec5661cd07fb414898c8b9
Hsuan Su
claimed_verified
2025-10-13T10:06:10.398Z
608abf1272b50b02c4b02865
https://cdn-avatars.huggingface.co/v1/production/uploads/1619708309549-608abf1272b50b02c4b02865.jpeg
Hsuan Su
user
jacksukk
68ec5661cd07fb414898c8ba
Tzu-Quan Lin
68ec5661cd07fb414898c8bb
Shang-Tse Chen
68ec5661cd07fb414898c8bc
Yun-Nung Chen
68ec5661cd07fb414898c8bd
Hung-yi Lee
Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic
  Speech Recognition
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.
A parameter-space correction method reduces Word Error Rate in ASR systems by addressing pseudo-label biases without target ground truth.
ASR domain shift pseudo-labeling parameter-space correction fine-tuning correction vector Word Error Rate (WER) AfriSpeech-200 Whisper tiny model