14 citations · 31 across the 5 of their papers we have counts for
6 papers
Chinese Song Iambics Generation with Neural Attention-based Model
Qixin Wang, Tianyi Luo, Dong Wang +1
Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they…
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…
Learning Speech Rate in Speech Recognition
Xiangyu Zeng, Shi Yin, Dong Wang
A significant performance reduction is often observed in speech recognition when the rate of speech (ROS) is too low or too high. Most of present approaches to addressing the ROS v…
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…