most citedImproving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model

6 citations · 6 across the 2 of their papers we have counts for

collaborators

5 papers

physics.chem-ph20244 cited

Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective

Yuzhi Xu, Haowei Ni, Qinhui Gao +10

Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to c…

eess.IV2023

Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label

Yike Zhang, Jack Noble

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries…

eess.AS2022

Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR

Kun Wei, Yike Zhang, Sining Sun +2

Leveraging context information is an intuitive idea to improve performance on conversational automatic speech recognition(ASR). Previous works usually adopt recognized hypotheses o…

cs.SD2022

Censer: Curriculum Semi-supervised Learning for Speech Recognition Based on Self-supervised Pre-training

Bowen Zhang, Songjun Cao, Xiaoming Zhang +3

Recent studies have shown that the benefits provided by self-supervised pre-training and self-training (pseudo-labeling) are complementary. Semi-supervised fine-tuning strategies u…

eess.AS20216 cited

Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model

Keqi Deng, Songjun Cao, Yike Zhang +1

Recently, self-supervised pretraining has achieved impressive results in end-to-end (E2E) automatic speech recognition (ASR). However, the dominant sequence-to-sequence (S2S) E2E m…