World's Best Scientists 2026 revealed!
Douglas A. Reynolds

Douglas A. Reynolds

D-Index & Metrics

Computer Science

D-Index
68
Citations
37646
World Ranking
2021
National Ranking
1023

Douglas A. Reynolds 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 Douglas A. Reynolds 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: 147 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

Douglas A. Reynolds 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 Douglas A. Reynolds 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: 68 D-Index — 86th percentile

86% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2010 - IEEE Fellow For contributions to Gaussian-mixture-model techniques for automatic speaker recognition

Overview

Douglas A. Reynolds is a researcher affiliated with MIT in the United States, focusing primarily on areas within computer science. Their work spans several subfields including artificial intelligence and signal processing. The main topics featured in their research include speech recognition and synthesis, speech and audio processing, natural language processing techniques, music and audio processing, and wireless signal modulation classification.

Their frequent publication venues reflect a focus on audio and speech processing topics, with multiple papers in arXiv (Cornell University), and contributions to IEEE/ACM Transactions on Audio Speech and Language Processing and Computer Speech & Language.

Some of their recent papers include the following:

  • "Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals" (2020), published in IEEE/ACM Transactions on Audio Speech and Language Processing
  • "VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge" (2020), published in arXiv (Cornell University)
  • "Two decades into Speaker Recognition Evaluation - are we there yet?" (2020), published in Computer Speech & Language
  • "The 2021 NIST Speaker Recognition Evaluation" (2022), published in arXiv (Cornell University)
  • "The NIST CTS Speaker Recognition Challenge" (2022), published in arXiv (Cornell University)

Collaborations have been frequent with researchers across related domains. Prominent coauthors include Craig S. Greenberg, Elliot Singer, Lisa Reyes Mason, Kong Aik Lee, and Seyed Omid Sadjadi.

Douglas A. Reynolds has been recognized by the IEEE with the IEEE Fellow distinction awarded in 2010 for contributions to Gaussian-mixture-model techniques in automatic speaker recognition.

Best Publications

  • Speaker Verification Using Adapted Gaussian Mixture Models

    Douglas A. Reynolds;Thomas F. Quatieri;Robert B. Dunn

  • Robust text-independent speaker identification using Gaussian mixture speaker models

    D.A. Reynolds;R.C. Rose

  • Gaussian Mixture Models

    Unknown

  • Speaker identification and verification using Gaussian mixture speaker models

    Douglas A. Reynolds

  • Gaussian Mixture Models.

    Douglas A. Reynolds

  • Support vector machines using GMM supervectors for speaker verification

    W.M. Campbell;D.E. Sturim;D.A. Reynolds

  • A tutorial on text-independent speaker verification

    Frédéric Bimbot;Jean-François Bonastre;Corinne Fredouille;Guillaume Gravier

  • An overview of automatic speaker recognition technology

    Douglas A. Reynolds

  • SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

    W.M. Campbell;D.E. Sturim;D.A. Reynolds;A. Solomonoff

  • An overview of automatic speaker diarization systems

    S.E. Tranter;D.A. Reynolds

  • Support vector machines for speaker and language recognition

    William M. Campbell;Joseph P. Campbell;Douglas A. Reynolds;Elliot Singer

  • Approaches to Language Identification using Gaussian Mixture Models and Shifted Delta Cepstral Features

    Pedro A. Torres-Carrasquillo;Pedro A. Torres-Carrasquillo;Elliot Singer;Mary A. Kohler;Richard J. Greene

  • Sheep, Goats, Lambs and Wolves: A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation

    George R. Doddington;Walter Liggett;Alvin F. Martin;Mark A. Przybocki

  • Language Recognition via i-vectors and Dimensionality Reduction.

    Najim Dehak;Pedro A. Torres-Carrasquillo;Douglas A. Reynolds;Réda Dehak

  • Comparison of background normalization methods for text-independent speaker verification.

    Douglas A. Reynolds

  • Deep Neural Network Approaches to Speaker and Language Recognition

    Fred Richardson;Douglas Reynolds;Najim Dehak

  • Experimental evaluation of features for robust speaker identification

    D.A. Reynolds

  • The NIST speaker recognition evaluation - overview methodology, systems, results, perspective

    Douglas A. Reynolds;George R. Doddington;George R. Doddington;Mark A. Przybocki;Alvin F. Martin

  • A Gaussian mixture modeling approach to text-independent speaker identification

    Douglas A. Reynolds

  • Modeling of the glottal flow derivative waveform with application to speaker identification

    M.D. Plumpe;T.F. Quatieri;D.A. Reynolds

  • Language Recognition via Ivectors and Dimensionality Reduction

    Najim Dehak;Pedro A. Torres-Carrasquillo;Douglas Reynolds;Reda Dehak

Frequent Co-Authors

William M. Campbell
William M. Campbell Amazon (United States)
Najim Dehak
Najim Dehak Johns Hopkins University
Alan V. McCree
Alan V. McCree Johns Hopkins University
George R. Doddington
George R. Doddington Texas Instruments (United States)
Tomi Kinnunen
Tomi Kinnunen University of Eastern Finland
Daniel Garcia-Romero
Daniel Garcia-Romero Johns Hopkins University
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics

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