4 papers
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…
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
Hua Farn, Hsuan Su, Shachi H Kumar +3
Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of originally aligned models. While some existing…
Identifying Speaker Information in Feed-Forward Layers of Self-Supervised Speech Transformers
Tzu-Quan Lin, Hsi-Chun Cheng, Hung-yi Lee +1
In recent years, the impact of self-supervised speech Transformers has extended to speaker-related applications. However, little research has explored how these models encode speak…
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition
Hsuan Su, Hua Farn, Fan-Yun Sun +2
Synthetic data is widely used in speech recognition due to the availability of text-to-speech models, which facilitate adapting models to previously unseen text domains. However, e…