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D-Index & Metrics

Computer Science

D-Index
33
Citations
11114
World Ranking
12372
National Ranking
5012

Chung-Cheng Chiu 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 Chung-Cheng Chiu 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: 129 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Chung-Cheng Chiu 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 Chung-Cheng Chiu 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Chung-Cheng Chiu is a researcher affiliated with Google in the United States. Their work primarily centers on the field of Computer Science, with a significant focus on Artificial Intelligence and Signal Processing. The research contributions also extend into Computer Vision and Pattern Recognition, and to a lesser extent, Aerospace Engineering.

Their scholarly output includes numerous publications with a strong emphasis on speech and audio technologies. Main research topics involve Speech Recognition and Synthesis, Music and Audio Processing, and Speech and Audio Processing. Additional interests cover Natural Language Processing Techniques, Topic Modeling, Digital Media Forensic Detection, and Generative Adversarial Networks and Image Synthesis.

Chiu has published extensively in the following venues:

  • arXiv (Cornell University)
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • IEEE Journal of Selected Topics in Signal Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Applied Sciences

Some of the more recent papers authored or coauthored by Chung-Cheng Chiu include:

  • Conformer: Convolution-augmented Transformer for Speech Recognition (2020, arXiv (Cornell University))
  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU))
  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition (2020, arXiv (Cornell University))
  • BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2022, IEEE Journal of Selected Topics in Signal Processing)
  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (2023, arXiv (Cornell University))

The research collaborations include frequent coauthors such as Ruoming Pang, Yonghui Wu, Wei Han, James Qin, and Yu Zhang.

The areas of study and the research papers indicate a concentration on advancing automatic speech recognition technologies, combining methods like contrastive learning, masked language modeling, convolutional neural networks, and large-scale semi-supervised learning for enhanced audio processing systems.

Best Publications

  • SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

    Daniel S. Park;William Chan;Yu Zhang;Chung-Cheng Chiu

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

    Unknown

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • Improved Noisy Student Training for Automatic Speech Recognition

    Daniel S. Park;Yu Zhang;Ye Jia;Wei Han

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;James Qin;Daniel S. Park;Wei Han

  • Monotonic Chunkwise Attention

    Chung-Cheng Chiu;Colin Raffel

  • A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

  • Monotonic Infinite Lookback Attention for Simultaneous Machine Translation

    Naveen Arivazhagan;Colin Cherry;Wolfgang Macherey;Chung-Cheng Chiu

  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

    Unknown

  • Leveraging Weakly Supervised Data to Improve End-to-end Speech-to-text Translation

    Ye Jia;Melvin Johnson;Wolfgang Macherey;Ron J. Weiss

  • BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;Daniel S. Park;Wei Han;James Qin

  • Minimum Word Error Rate Training for Attention-Based Sequence-to-Sequence Models

    Rohit Prabhavalkar;Tara N. Sainath;Yonghui Wu;Patrick Nguyen

  • A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition

    Shubham Toshniwal;Anjuli Kannan;Chung-Cheng Chiu;Yonghui Wu

  • Specaugment on Large Scale Datasets

    Daniel S. Park;Yu Zhang;Chung-Cheng Chiu;Youzheng Chen

  • Two-Pass End-to-End Speech Recognition

    Sainath Tara C;Pang Ruoming;Rybach David;He Yanzhang

  • A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm

    Unknown

  • Recognizing Long-Form Speech Using Streaming End-to-End Models

    Arun Narayanan;Rohit Prabhavalkar;Chung-Cheng Chiu;David Rybach

  • A Better and Faster end-to-end Model for Streaming ASR

    Bo Li;Anmol Gulati;Jiahui Yu;Tara N. Sainath

  • Predicting Co-verbal Gestures: A Deep and Temporal Modeling Approach

    Chung-Cheng Chiu;Louis-Philippe Morency;Stacy Marsella

  • How to train your avatar: a data driven approach to gesture generation

    Chung-Cheng Chiu;Stacy Marsella

  • State-of-the-art Speech Recognition With Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • A Comparison of End-to-End Models for Long-Form Speech Recognition

    Chung-Cheng Chiu;Anjuli Kannan;Rohit Prabhavalkar;Zhifeng Chen

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

Frequent Co-Authors

Yonghui Wu
Yonghui Wu Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Bo Li
Bo Li University of Illinois at Urbana-Champaign
Patrick Nguyen
Patrick Nguyen Google (United States)
Zhifeng Chen
Zhifeng Chen Google (United States)
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Liangliang Cao
Liangliang Cao Google (United States)
Colin Raffel
Colin Raffel University of Toronto

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