most citedEMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition

3 citations · 3 across the 5 of their papers we have counts for

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eess.AS2025

Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition

Yi-Cheng Lin, Yu-Hsuan Li Liang, Hsuan Su +4

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 work…

eess.AS2025★ 3 cited

EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition

Yi-Cheng Lin, Huang-Cheng Chou, Yu-Hsuan Li Liang +1

Speech emotion recognition (SER) systems often exhibit gender bias. However, the effectiveness and robustness of existing debiasing methods in such multi-label scenarios remain und…