World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
41093
World Ranking
1311
National Ranking
693

Tara N. Sainath 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 Tara N. Sainath 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: 288 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Tara N. Sainath 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 Tara N. Sainath 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: 76 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Overview

Tara N. Sainath is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and signal processing. Their research predominantly centers around speech recognition and synthesis, speech and audio processing, and related areas such as music and audio processing and natural language processing techniques.

They have authored numerous publications including recent papers such as:

  • "Self-Supervised Speech Representation Learning: A Review" (2022, IEEE Journal of Selected Topics in Signal Processing)
  • "BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition" (2022, IEEE Journal of Selected Topics in Signal Processing)
  • "End-to-End Speech Recognition: A Survey" (2023, IEEE/ACM Transactions on Audio Speech and Language Processing)
  • "Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages" (2023, arXiv (Cornell University))
  • "Scaling End-to-End Models for Large-Scale Multilingual ASR" (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU))

Their frequent co-authors include Trevor Strohman, Rohit Prabhavalkar, Shuo-Yiin Chang, Bo Li, and Weiran Wang. These collaborations have supported research spanning large-scale speech recognition models and multilingual applications.

Sainath's work is often published in venues including:

  • arXiv (Cornell University)
  • Interspeech 2022
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2022 IEEE Spoken Language Technology Workshop (SLT)
  • IEEE Journal of Selected Topics in Signal Processing

Their contributions cover several subfields within computer science:

  • Artificial Intelligence
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Experimental and Cognitive Psychology
  • Control and Systems Engineering

Sainath's main research topics include:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Topic Modeling
  • Speech and Dialogue Systems
  • Phonetics and Phonology Research

Best Publications

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

    G. Hinton;Li Deng;Dong Yu;G. E. Dahl

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition

    Geoffrey Hinton;Li Deng;Dong Yu;George Dahl

  • Improving deep neural networks for LVCSR using rectified linear units and dropout

    George E. Dahl;Tara N. Sainath;Geoffrey E. Hinton

  • Deep Convolutional Neural Networks for Large-scale Speech Tasks

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

  • Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

    Tara N. Sainath;Oriol Vinyals;Andrew Senior;Hasim Sak

  • Deep convolutional neural networks for LVCSR

    Tara N. Sainath;Abdel-rahman Mohamed;Brian Kingsbury;Bhuvana Ramabhadran

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

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

  • Deep Learning for Audio Signal Processing

    Hendrik Purwins;Bo Li;Tuomas Virtanen;Jan Schluter

  • Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets

    Tara N. Sainath;Brian Kingsbury;Vikas Sindhwani;Ebru Arisoy

  • Streaming End-to-end Speech Recognition for Mobile Devices

    Yanzhang He;Tara N. Sainath;Rohit Prabhavalkar;Ian McGraw

  • Convolutional Neural Networks for Small-Footprint Keyword Spotting

    Tara N. Sainath;Carolina Parada

  • Learning the Speech Front-end with Raw Waveform CLDNNs

    Tara N. Sainath;Ron J. Weiss;Andrew W. Senior;Kevin W. Wilson

  • Self-Supervised Speech Representation Learning: A Review

    Unknown

  • Deep Belief Networks using discriminative features for phone recognition

    Abdel-rahman Mohamed;Tara N. Sainath;George Dahl;Bhuvana Ramabhadran

  • A Comparison of Sequence-to-Sequence Models for Speech Recognition

    Rohit Prabhavalkar;Kanishka Rao;Tara N. Sainath;Bo Li

  • Improvements to Deep Convolutional Neural Networks for LVCSR

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

  • Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization.

    Brian Kingsbury;Tara N. Sainath;Hagen Soltau

  • Deep Neural Network Language Models

    Ebru Arisoy;Tara N. Sainath;Brian Kingsbury;Bhuvana Ramabhadran

  • Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition

    Tara N. Sainath;Ron J. Weiss;Kevin W. Wilson;Bo Li

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

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

    Anjuli Kannan;Yonghui Wu;Patrick Nguyen;Tara N. Sainath

  • The shared views of four research groups )

    Geoffrey Hinton;Li Deng;Dong Yu;George E. Dahl

Frequent Co-Authors

Dimitri Kanevsky
Dimitri Kanevsky Google (United States)
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Yonghui Wu
Yonghui Wu Google (United States)
Bo Li
Bo Li University of Illinois at Urbana-Champaign
Ruoming Pang
Ruoming Pang Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Brian Kingsbury
Brian Kingsbury IBM (United States)
Michiel Bacchiani
Michiel Bacchiani Google (United States)

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