World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
8834
World Ranking
8290
National Ranking
3555

George Saon 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 George Saon 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: 158 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

George Saon 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 George Saon 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

George Saon is affiliated with IBM in the United States. Their research is primarily situated within the field of Computer Science, with a focus on Artificial Intelligence, Signal Processing, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, and Information Systems.

Their work extensively covers various topics, including:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Music and Audio Processing
  • Speech and dialogue systems
  • Neural Networks and Applications

George Saon has contributed to multiple papers of relevance within the speech recognition and acoustic modeling communities. Some recent publications include:

  • Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition: A comparison of current training strategies, 2020, IEEE Signal Processing Magazine
  • Integrating Text Inputs for Training and Adapting RNN Transducer ASR Models, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems, 2022, Interspeech 2022
  • Towards Reducing the Need for Speech Training Data to Build Spoken Language Understanding Systems, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Speech Recognition Using Biologically-Inspired Neural Networks, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

They frequently collaborate with other researchers such as Brian Kingsbury, Xiaodong Cui, Samuel Thomas, Hong-Kwang Jeff Kuo, and Gakuto Kurata.

The venues where George Saon publishes most include:

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

Their contributions span areas of research that integrate deep neural network training strategies, the application of recurrent neural networks in speech recognition models, improvements in language model rescoring, and methodologies to reduce training data requirements for spoken language understanding systems.

Best Publications

  • Deep Convolutional Neural Networks for Large-scale Speech Tasks

    Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau

  • Speaker adaptation of neural network acoustic models using i-vectors

    George Saon;Hagen Soltau;David Nahamoo;Michael Picheny

  • Boosted MMI for model and feature-space discriminative training

    D. Povey;D. Kanevsky;B. Kingsbury;B. Ramabhadran

  • fMPE: discriminatively trained features for speech recognition

    D. Povey;B. Kingsbury;L. Mangu;G. Saon

  • English Conversational Telephone Speech Recognition by Humans and Machines

    George Saon;Gakuto Kurata;Tom Sercu;Kartik Audhkhasi

  • Maximum likelihood discriminant feature spaces

    G. Saon;M. Padmanabhan;R. Gopinath;S. Chen

  • Improvements to Deep Convolutional Neural Networks for LVCSR

    Tara N. Sainath;Brian Kingsbury;Abdel-rahman Mohamed;George E. Dahl

  • The IBM 2016 English Conversational Telephone Speech Recognition System

    George Saon;Tom Sercu;Steven J. Rennie;Hong-Kwang Jeff Kuo

  • Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances

    G. Saon;Jen-Tzung Chien

  • The IBM Attila speech recognition toolkit

    Hagen Soltau;George Saon;Brian Kingsbury

  • Advances in speech transcription at IBM under the DARPA EARS program

    S.F. Chen;B. Kingsbury;Lidia Mangu;D. Povey

  • The IBM 2004 conversational telephony system for rich transcription

    H. Soltau;B. Kingsbury;L. Mangu;D. Povey

  • The IBM 2015 English Conversational Telephone Speech Recognition System

    George Saon;Hong-Kwang Jeff Kuo;Steven J. Rennie;Michael Picheny

  • Analyzing convolutional neural networks for speech activity detection in mismatched acoustic conditions

    Samuel Thomas;Sriram Ganapathy;George Saon;Hagen Soltau

  • Direct Acoustics-to-Word Models for English Conversational Speech Recognition

    Kartik Audhkhasi;Bhuvana Ramabhadran;George Saon;Michael Picheny

  • Building Competitive Direct Acoustics-to-Word Models for English Conversational Speech Recognition

    Kartik Audhkhasi;Brian Kingsbury;Bhuvana Ramabhadran;George Saon

  • Joint training of convolutional and non-convolutional neural networks

    Hagen Soltau;George Saon;Tara N. Sainath

  • Anatomy of an extremely fast LVCSR decoder.

    George Saon;Daniel Povey;Geoffrey Zweig

  • Feature and model space speaker adaptation with full covariance Gaussians.

    Daniel Povey;George Saon

  • Data-driven approach to designing compound words for continuous speech recognition

    G. Saon;M. Padmanabhan

Frequent Co-Authors

Brian Kingsbury
Brian Kingsbury IBM (United States)
Michael Picheny
Michael Picheny IBM (United States)
Hagen Soltau
Hagen Soltau Google (United States)
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Daniel Povey
Daniel Povey Xiaomi (China)
Samuel Thomas
Samuel Thomas IBM (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Jen-Tzung Chien
Jen-Tzung Chien National Yang Ming Chiao Tung University
David Nahamoo
David Nahamoo Pyron Inc.
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)

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