3 papers
cs.CL2026
Adapting Text LLMs to Speech via Multimodal Depth Up-Scaling
Kazuki Yano, Jun Suzuki, Shinji Watanabe
Adapting pre-trained text Large Language Models (LLMs) into Speech Language Models (Speech LMs) via continual pretraining on speech data is promising, but often degrades the origin…
cs.SD2026
SSVD-O: Parameter-Efficient Fine-Tuning with Structured SVD for Speech Recognition
Pu Wang, Shinji Watanabe, Hugo Van hamme
Parameter-efficient fine-tuning (PEFT) is a scalable approach for adapting large speech foundation models to new domains. While methods such as LoRA and its state-of-the-art varian…
cs.CL2025
SSVD: Structured SVD for Parameter-Efficient Fine-Tuning and Benchmarking under Domain Shift in ASR
Pu Wang, Shinji Watanabe, Hugo Van hamme
Parameter-efficient fine-tuning (PEFT) has emerged as a scalable solution for adapting large foundation models. While low-rank adaptation (LoRA) is widely used in speech applicatio…