World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
9670
World Ranking
7871
National Ranking
1034

Li-Rong Dai publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Li-Rong Dai sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 341 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Li-Rong Dai D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Li-Rong Dai sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

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