125 citations · 154 across the 4 of their papers we have counts for
4 papers
Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model
Xu Tian, Jun Zhang, Zejun Ma +2
Frame stacking is broadly applied in end-to-end neural network training like connectionist temporal classification (CTC), and it leads to more accurate models and faster decoding.…
Deep LSTM for Large Vocabulary Continuous Speech Recognition
Xu Tian, Jun Zhang, Zejun Ma +6
Recurrent neural networks (RNNs), especially long short-term memory (LSTM) RNNs, are effective network for sequential task like speech recognition. Deeper LSTM models perform well…
Exponential Moving Average Model in Parallel Speech Recognition Training
Xu Tian, Jun Zhang, Zejun Ma +2
As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently.We present a new approach that…
Implementation of Training Convolutional Neural Networks
Tianyi Liu, Shuangsang Fang, Yuehui Zhao +2
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural n…