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eess.AS2023
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…
eess.AS2022
UFO2: A unified pre-training framework for online and offline speech recognition
Li Fu, Siqi Li, Qingtao Li +5
In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows…
eess.AS2021
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…