6 papers · 1 filter
QuaSR: Quality-Aware Sample Reweighting for Pacific Indigenous Speech Recognition
Yishun Li, Yang Xiao, Gongping Huang +3
Training automatic speech recognition (ASR) models for low-resource languages is challenging due to limited data and highly variable supervision quality. In particular, Pacific Ind…
Rethinking Continual Learning for Speech and Audio: A Representation-Centric Taxonomy and Open Problems
Yang Xiao, Siyi Wang, Eun-Jung Holden +1
Speech and audio systems operate in inherently non-stationary environments, yet continual learning (CL) research in this domain, especially in the foundation model era, remains fra…
Why Can't They Remember? Uncovering Representation and Retrieval Bottlenecks in Multi-Turn Acoustic Memory
Yang Xiao, Siyi Wang, Han Yin +4
Large audio language models (LALMs) process both speech and environmental acoustic cues, yet struggle to retain non-speech information across multi-turn interactions. The performan…
Continual Adaptation for Pacific Indigenous Speech Recognition
Yang Xiao, Aso Mahmudi, Nick Thieberger +3
Speech foundation models struggle with low-resource Pacific Indigenous languages because of severe data scarcity. Furthermore, full fine-tuning risks catastrophic forgetting. To ad…
Activation Steering for Accent Adaptation in Large Audio Language Models
Jinuo Sun, Yang Xiao, Sung Kyun Chung +4
Accent variability remains a major source of errors in automatic speech recognition, yet most adaptation methods rely on parameter fine-tuning without understanding where accent in…
Adapting Where It Matters: Depth-Aware Adaptation for Efficient Multilingual Speech Recognition in Low-Resource Languages
Yang Xiao, Eun-Jung Holden, Ting Dang
Recent speech foundation models excel at multilingual automatic speech recognition (ASR) for high-resource languages, but adapting them to low-resource languages remains challengin…