2 citations · 3 across the 3 of their papers we have counts for
4 papers
PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
Li Fu, Yu Xin, Sunlu Zeng +3
This paper presents a Pronunciation-Aware Contextualized (PAC) framework to address two key challenges in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) system…
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition
Li Fu, Shanyong Yu, Siqi Li +3
Recent advancements in scaling up models have significantly improved performance in Automatic Speech Recognition (ASR) tasks. However, training large ASR models from scratch remain…
Do self-supervised speech and language models extract similar representations as human brain?
Peili Chen, Linyang He, Li Fu +3
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given the…
Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstruction
Jiawei Li, Chunxu Guo, Li Fu +3
Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural…