5 papers
When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Chen-An Li, Tzu-Han Lin, Hung-yi Lee
Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We investigate how irrelevant audio,…
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques
Tzu-Han Lin, How-Shing Wang, Hao-Yung Weng +3
Parameter-Efficient Fine-Tuning (PEFT) is increasingly recognized as an effective method in speech processing. However, the optimal approach and the placement of PEFT methods remai…
On the Utility of Self-supervised Models for Prosody-related Tasks
Guan-Ting Lin, Chi-Luen Feng, Wei-Ping Huang +5
Self-Supervised Learning (SSL) from speech data has produced models that have achieved remarkable performance in many tasks, and that are known to implicitly represent many aspects…
TeleSHift: Telexisting TUI for Physical Collaboration & Interaction
Andrew Chen, Tzu-Ling Yang, Shu-Yan Cheng +3
Current workflow on co-editing and simultaneous presentation of 3-D shapes is confined to on-screen manipulation, which causes loss of perceived information when presenting percept…
The Efficacy of Self-Supervised Speech Models for Audio Representations
Tung-Yu Wu, Chen-An Li, Tzu-Han Lin +2
Self-supervised learning (SSL) speech models, which can serve as powerful upstream models to extract meaningful speech representations, have achieved unprecedented success in speec…