58 citations · 74 across the 3 of their papers we have counts for
3 papers
cs.CL2021
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-resource Speech Recognition
Cheng Yi, Shiyu Zhou, Bo Xu
End-to-end models have achieved impressive results on the task of automatic speech recognition (ASR). For low-resource ASR tasks, however, labeled data can hardly satisfy the deman…
cs.CL2021★ 58 cited
Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
Cheng Yi, Jianzhong Wang, Ning Cheng +2
There are several domains that own corresponding widely used feature extractors, such as ResNet, BERT, and GPT-x. These models are usually pre-trained on large amounts of unlabeled…
eess.AS2020★ 16 cited
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition
Linhao Dong, Cheng Yi, Jianzong Wang +4
End-to-end models are gaining wider attention in the field of automatic speech recognition (ASR). One of their advantages is the simplicity of building that directly recognizes the…