World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
9670
World Ranking
7872
National Ranking
1034

Overview

Li-Rong Dai is affiliated with the University of Science and Technology of China. Their research primarily focuses on computer science, with a significant emphasis on signal processing and artificial intelligence. The scientist's work spans various subfields including computer vision and pattern recognition, computational mechanics, and cognitive neuroscience.

Their publications address several main topics such as:

  • Speech and Audio Processing
  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Advanced Adaptive Filtering Techniques
  • Handwritten Text Recognition Techniques

Li-Rong Dai has contributed to numerous research venues, frequently publishing in:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Interspeech 2022
  • IEEE Transactions on Multimedia

Collaborations form a notable part of their work. Frequent co-authors include Jie Zhang, Ziqiang Zhang, Jun Du, Yan Song, and Qiushi Zhu.

Some of their recent papers illustrate the scope of their research:

  • "Radical analysis network for learning hierarchies of Chinese characters," 2020, Pattern Recognition
  • "A Joint Speech Enhancement and Self-Supervised Representation Learning Framework for Noise-Robust Speech Recognition," 2023, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "SRD: A Tree Structure Based Decoder for Online Handwritten Mathematical Expression Recognition," 2020, IEEE Transactions on Multimedia
  • "A Noise-Robust Self-Supervised Pre-Training Model Based Speech Representation Learning for Automatic Speech Recognition," 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "A multimodal attention fusion network with a dynamic vocabulary for TextVQA," 2021, Pattern Recognition

Best Publications

  • A regression approach to speech enhancement based on deep neural networks

    Yong Xu;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • An Experimental Study on Speech Enhancement Based on Deep Neural Networks

    Yong Xu;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • Voice conversion using deep neural networks with layer-wise generative training

    Ling-Hui Chen;Zhen-Hua Ling;Li-Juan Liu;Li-Rong Dai

  • Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition

    Jianshu Zhang;Jun Du;Shiliang Zhang;Dan Liu

  • Multiple-target deep learning for LSTM-RNN based speech enhancement

    Lei Sun;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • An Attention Pooling based Representation Learning Method for Speech Emotion Recognition

    Pengcheng Li;Yan Song;Ian Vince McLoughlin;Wu Guo

  • Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition

    Jianshu Zhang;Jun Du;Lirong Dai

  • Fast adaptation of deep neural network based on discriminant codes for speech recognition

    Shaofei Xue;Ossama Abdel-Hamid;Hui Jiang;Lirong Dai

  • Deep-FSMN for Large Vocabulary Continuous Speech Recognition

    Unknown

  • Optimizing multi-graph learning: towards a unified video annotation scheme

    Meng Wang;Xian-Sheng Hua;Xun Yuan;Yan Song

  • WaveNet Vocoder with Limited Training Data for Voice Conversion.

    Li-Juan Liu;Zhen-Hua Ling;Yuan Jiang;Ming Zhou

  • Robust speech recognition with speech enhanced deep neural networks.

    Jun Du;Qing Wang;Tian Gao;Yong Xu

  • Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations

    Jing-Xuan Zhang;Zhen-Hua Ling;Li-Rong Dai

  • Track, Attend, and Parse (TAP): An End-to-End Framework for Online Handwritten Mathematical Expression Recognition

    Jianshu Zhang;Jun Du;Lirong Dai

  • Dynamic noise aware training for speech enhancement based on deep neural networks.

    Yong Xu;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • A regression approach to single-channel speech separation via high-resolution deep neural networks

    Jun Du;Yanhui Tu;Li-Rong Dai;Chin-Hui Lee

  • SNR-Based Progressive Learning of Deep Neural Network for Speech Enhancement.

    Tian Gao;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • Forward Attention in Sequence- To-Sequence Acoustic Modeling for Speech Synthesis

    Jing-Xuan Zhang;Zhen-Hua Ling;Li-Rong Dai

  • Deep Bottleneck Features for Spoken Language Identification

    Unknown

  • The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models

    ShiLiang Zhang;Hui Jiang;MingBin Xu;JunFeng Hou

  • Semi-supervised kernel density estimation for video annotation

    Meng Wang;Xian-Sheng Hua;Tao Mei;Richang Hong

  • Densely Connected Progressive Learning for LSTM-Based Speech Enhancement

    Tian Gao;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • Sequence-to-Sequence Acoustic Modeling for Voice Conversion

    Jing-Xuan Zhang;Zhen-Hua Ling;Li-Juan Liu;Yuan Jiang

Frequent Co-Authors

Zhen-Hua Ling
Zhen-Hua Ling University of Science and Technology of China
Jun Du
Jun Du University of Science and Technology of China
Chin-Hui Lee
Chin-Hui Lee Georgia Institute of Technology
Hui Jiang
Hui Jiang York University
Haizhou Li
Haizhou Li Chinese University of Hong Kong, Shenzhen
Xian-Sheng Hua
Xian-Sheng Hua Terminus International
Frank K. Soong
Frank K. Soong Microsoft Research Asia (China)
Eng Siong Chng
Eng Siong Chng Nanyang Technological University
Lei He
Lei He University of California, Los Angeles
Yongxin Yang
Yongxin Yang Queen Mary University of London

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens many doors. If you’re interested in interdisciplinary fields, you might be curious about what jobs can you get with an environmental science degree. Careers range from data analysis to environmental consulting and research roles, offering flexibility beyond traditional tech jobs.

For those looking for a faster route into the field, computer science accelerated program options are available online. These programs are designed for motivated students who want to earn their degree in less time and begin their careers quicker.

Related technical disciplines are also accessible through remote learning. Consider pursuing an online environmental engineering degree or an online degree in mechanical engineering. These programs cover essential engineering foundations and open up additional pathways in technology, sustainability, and industry.

Whether you want to specialize or broaden your expertise, online degrees offer flexibility and affordability for future-focused careers in science and engineering.

Best Scientists Citing Li-Rong Dai

Trending Scientists

Recently Published Articles