10 citations · 14 across the 6 of their papers we have counts for
7 papers
Exploiting Single-Channel Speech For Multi-channel End-to-end Speech Recognition
Keyu An, Zhijian Ou
Recently, the end-to-end training approach for neural beamformer-supported multi-channel ASR has shown its effectiveness in multi-channel speech recognition. However, the integrati…
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
Chengrui Zhu, Keyu An, Huahuan Zheng +1
The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual…
Deformable TDNN with adaptive receptive fields for speech recognition
Keyu An, Yi Zhang, Zhijian Ou
Time Delay Neural Networks (TDNNs) are widely used in both DNN-HMM based hybrid speech recognition systems and recent end-to-end systems. Nevertheless, the receptive fields of TDNN…
The SLT 2021 children speech recognition challenge: Open datasets, rules and baselines
Fan Yu, Zhuoyuan Yao, Xiong Wang +6
Automatic speech recognition (ASR) has been significantly advanced with the use of deep learning and big data. However improving robustness, including achieving equally good perfor…
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients
Huahuan Zheng, Keyu An, Zhijian Ou
Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacin…
CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency
Keyu An, Hongyu Xiang, Zhijian Ou
In this paper, we present a new open source toolkit for speech recognition, named CAT (CTC-CRF based ASR Toolkit). CAT inherits the data-efficiency of the hybrid approach and the s…