14 citations · 32 across the 6 of their papers we have counts for
6 papers
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition
Xulong Zhang, Jianzong Wang, Ning Cheng +3
The recent emergence of joint CTC-Attention model shows significant improvement in automatic speech recognition (ASR). The improvement largely lies in the modeling of linguistic in…
Large-scale Transfer Learning for Low-resource Spoken Language Understanding
Xueli Jia, Jianzong Wang, Zhiyong Zhang +2
End-to-end Spoken Language Understanding (SLU) models are made increasingly large and complex to achieve the state-ofthe-art accuracy. However, the increased complexity of a model…
Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition
Lantian Li, Yiye Lin, Zhiyong Zhang +1
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker…
Recognize Foreign Low-Frequency Words with Similar Pairs
Xi Ma, Xiaoxi Wang, Dong Wang +1
Low-frequency words place a major challenge for automatic speech recognition (ASR). The probabilities of these words, which are often important name entities, are generally under-e…
Knowledge Transfer Pre-training
Zhiyuan Tang, Dong Wang, Yiqiao Pan +1
Pre-training is crucial for learning deep neural networks. Most of existing pre-training methods train simple models (e.g., restricted Boltzmann machines) and then stack them layer…
Deep Speaker Vectors for Semi Text-independent Speaker Verification
Lantian Li, Dong Wang, Zhiyong Zhang +1
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker veri…