93 citations · 465 across the 63 of their papers we have counts for
10 papers · 1 filter
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…
Adversarial Data Augmentation for Disordered Speech Recognition
Zengrui Jin, Mengzhe Geng, Xurong Xie +4
Automatic recognition of disordered speech remains a highly challenging task to date. The underlying neuro-motor conditions, often compounded with co-occurring physical disabilitie…
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
Xu Li, Xixin Wu, Hui Lu +2
Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. The Res2Net approach designs a residual-like connection…
VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis
Hui Lu, Zhiyong Wu, Xixin Wu +4
This paper describes a variational auto-encoder based non-autoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequence-to-sequence…
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
Disong Wang, Liqun Deng, Yu Ting Yeung +3
One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech…
Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization
Disong Wang, Liqun Deng, Yu Ting Yeung +3
Dysarthric speech detection (DSD) systems aim to detect characteristics of the neuromotor disorder from speech. Such systems are particularly susceptible to domain mismatch where t…
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…