35 citations · 79 across the 6 of their papers we have counts for
6 papers
Doubly Robust Self-Training
Banghua Zhu, Mingyu Ding, Philip Jacobson +4
Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited lab…
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition
Ye-Qian Du, Jie Zhang, Qiu-Shi Zhu +4
Unpaired data has shown to be beneficial for low-resource automatic speech recognition~(ASR), which can be involved in the design of hybrid models with multi-task training or langu…
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition
Zi-Qiang Zhang, Jie Zhang, Jian-Shu Zhang +3
With the advance in self-supervised learning for audio and visual modalities, it has become possible to learn a robust audio-visual speech representation. This would be beneficial…
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition
Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +3
Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.…
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition
Zi-Qiang Zhang, Yan Song, Ming-Hui Wu +2
In this paper, we propose a weakly supervised multilingual representation learning framework, called cross-lingual self-training (XLST). XLST is able to utilize a small amount of a…
Adaptive Self-training for Few-shot Neural Sequence Labeling
Yaqing Wang, Subhabrata Mukherjee, Haoda Chu +4
Sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and s…