World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
9566
World Ranking
12382
National Ranking
5018

Daniel Garcia-Romero 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 Daniel Garcia-Romero 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Daniel Garcia-Romero 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 Daniel Garcia-Romero 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

Daniel Garcia-Romero is affiliated with Johns Hopkins University in the United States. Their research is primarily situated within the field of computer science, with specific emphasis on artificial intelligence and signal processing. Their work intersects several areas related to speech and audio technologies.

The main topics explored by Garcia-Romero include:

  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Speech and Audio Processing
  • Natural Language Processing Techniques
  • Adversarial Robustness in Machine Learning

Garcia-Romero has authored and contributed to multiple publications, with notable papers including:

  • Recent Developments on ESPnet Toolkit Boosted by Conformer, 2020, arXiv (Cornell University)
  • The VoxCeleb Speaker Recognition Challenge: A Retrospective, 2024, IEEE/ACM Transactions on Audio Speech and Language Processing
  • VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge, 2023, arXiv (Cornell University)
  • Directed speech separation for automatic speech recognition of long form conversational speech, 2022, Interspeech 2022
  • VoxWatch: An open-set speaker recognition benchmark on VoxCeleb, 2023, arXiv (Cornell University)

The frequent co-authors collaborating with Garcia-Romero include:

  • Katrin Kirchhoff
  • Sundararajan Srinivasan
  • Jaesung Huh
  • Joon Son Chung
  • Arsha Nagrani

Garcia-Romero's research findings have been disseminated predominantly through the following venues:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • Interspeech 2022

Best Publications

  • X-Vectors: Robust DNN Embeddings for Speaker Recognition

    David Snyder;Daniel Garcia-Romero;Gregory Sell;Daniel Povey

  • Analysis of i-vector Length Normalization in Speaker Recognition Systems.

    Daniel Garcia-Romero;Carol Y. Espy-Wilson

  • Deep Neural Network Embeddings for Text-Independent Speaker Verification.

    David Snyder;Daniel Garcia-Romero;Daniel Povey;Sanjeev Khudanpur

  • Deep neural network-based speaker embeddings for end-to-end speaker verification

    David Snyder;Pegah Ghahremani;Daniel Povey;Daniel Garcia-Romero

  • Speaker Recognition for Multi-speaker Conversations Using X-vectors

    David Snyder;Daniel Garcia-Romero;Gregory Sell;Alan McCree

  • Speaker diarization using deep neural network embeddings

    Daniel Garcia-Romero;David Snyder;Gregory Sell;Daniel Povey

  • Spoken Language Recognition using X-vectors.

    David Snyder;Daniel Garcia-Romero;Alan McCree;Gregory Sell

  • Speaker diarization with plda i-vector scoring and unsupervised calibration

    Gregory Sell;Daniel Garcia-Romero

  • Diarization is hard: Some experiences and lessons learned for the JHU team in the inaugural dihard challenge

    Gregory Sell;David Snyder;Alan McCree;Daniel Garcia-Romero

  • Recent Developments on Espnet Toolkit Boosted By Conformer

    Pengcheng Guo;Florian Boyer;Xuankai Chang;Tomoki Hayashi

  • Linear versus mel frequency cepstral coefficients for speaker recognition

    Xinhui Zhou;Daniel Garcia-Romero;Ramani Duraiswami;Carol Espy-Wilson

  • Time delay deep neural network-based universal background models for speaker recognition

    David Snyder;Daniel Garcia-Romero;Daniel Povey

  • A comparative evaluation of fusion strategies for multimodal biometric verification

    J. Fierrez-Aguilar;J. Ortega-Garcia;D. Garcia-Romero;J. Gonzalez-Rodriguez

  • Supervised domain adaptation for I-vector based speaker recognition

    Daniel Garcia-Romero;Alan McCree

  • Multicondition training of Gaussian PLDA models in i-vector space for noise and reverberation robust speaker recognition

    Daniel Garcia-Romero;Xinhui Zhou;Carol Y. Espy-Wilson

  • UNSUPERVISED DOMAIN ADAPTATION FOR I-VECTOR SPEAKER RECOGNITION

    Niko Brummer;Alan McCree;Stephen Shum;Daniel Garcia-Romero

  • State-of-the-art speaker recognition with neural network embeddings in NIST SRE18 and Speakers in the Wild evaluations

    Jesús Villalba;Nanxin Chen;David Snyder;Daniel Garcia-Romero

  • Adapted user-dependent multimodal biometric authentication exploiting general information

    Julian Fierrez-Aguilar;Daniel Garcia-Romero;Javier Ortega-Garcia;Joaquin Gonzalez-Rodriguez

  • The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge.

    Alan McCree;Douglas A. Reynolds;Daniel Garcia-Romero;Tomi Kinnunen

  • Automatic acquisition device identification from speech recordings

    Daniel Garcia-Romero;Carol Y. Espy-Wilson

  • Linear versus Mel Frequency Cepstral Coefficients for Speaker Recognition (Author's Manuscript)

    Xinhui Zhou;Daniel Garcia-Romero;Ramani Duraiswami;Carol Espy-Wilson

Frequent Co-Authors

Alan V. McCree
Alan V. McCree Johns Hopkins University
Carol Y. Espy-Wilson
Carol Y. Espy-Wilson University of Maryland, College Park
Daniel Povey
Daniel Povey Xiaomi (China)
Javier Ortega-Garcia
Javier Ortega-Garcia Autonomous University of Madrid
Joaquin Gonzalez-Rodriguez
Joaquin Gonzalez-Rodriguez Autonomous University of Madrid
Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University
Ramani Duraiswami
Ramani Duraiswami University of Maryland, College Park
Najim Dehak
Najim Dehak Johns Hopkins University
Shihab A. Shamma
Shihab A. Shamma University of Maryland, College Park

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing a Computer Science degree in the USA opens doors to a wide range of career options, especially with the growth of online education. Many college with low gpa opportunities make it possible for students from various academic backgrounds to begin their journey in tech fields.

For those seeking rapid entry into the workforce, choosing a fastest computer science degree can help you graduate sooner and start building your career quickly. These accelerated programs combine flexibility with rigorous coursework, making them ideal for motivated learners.

Additionally, tech-related knowledge pairs well with other growing sectors, such as environmental science. If you’re interested in sustainability, you might explore "what can you do with an environmental studies degree" (what can you do with an environmental studies degree) to see crossover roles in data analysis, project management, and policy development.

For hands-on, practical roles, consider an environmental engineering degree online. This could prepare you for high-demand positions working on technology-driven solutions to environmental challenges, enhancing your career flexibility and impact.

Best Scientists Citing Daniel Garcia-Romero

Trending Scientists

Recently Published Articles