27 citations · 60 across the 30 of their papers we have counts for
4 papers · 1 filter
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…
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition
Li Fu, Siqi Li, Qingtao Li +6
Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings. However, the advantag…
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
Li Fu, Xiaoxiao Li, Runyu Wang +5
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervisio…
Incremental Learning for End-to-End Automatic Speech Recognition
Li Fu, Xiaoxiao Li, Libo Zi +4
In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintainin…