17 citations · 22 across the 7 of their papers we have counts for
7 papers
Bayesian Neural Network Language Modeling for Speech Recognition
Boyang Xue, Shoukang Hu, Junhao Xu +3
State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. Th…
Towards Green ASR: Lossless 4-bit Quantization of a Hybrid TDNN System on the 300-hr Switchboard Corpus
Junhao Xu, Shoukang Hu, Xunying Liu +1
State of the art time automatic speech recognition (ASR) systems are becoming increasingly complex and expensive for practical applications. This paper presents the development of…
Mixed Precision of Quantization of Transformer Language Models for Speech Recognition
Junhao Xu, Shoukang Hu, Jianwei Yu +2
State-of-the-art neural language models represented by Transformers are becoming increasingly complex and expensive for practical applications. Low-bit deep neural network quantiza…
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition
Junhao Xu, Jianwei Yu, Shoukang Hu +2
State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive…
Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers
Junhao Xu, Xie Chen, Shoukang Hu +3
The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent ye…
Mixed Precision DNN Qunatization for Overlapped Speech Separation and Recognition
Junhao Xu, Jianwei Yu, Xunying Liu +1
Recognition of overlapped speech has been a highly challenging task to date. State-of-the-art multi-channel speech separation system are becoming increasingly complex and expensive…
Bayesian Transformer Language Models for Speech Recognition
Boyang Xue, Jianwei Yu, Junhao Xu +6
State-of-the-art neural language models (LMs) represented by Transformers are highly complex. Their use of fixed, deterministic parameter estimates fail to account for model uncert…