World's Best Scientists 2026 revealed!
Daniel Povey

Daniel Povey

D-Index & Metrics

Computer Science

D-Index
67
Citations
37874
World Ranking
2136
National Ranking
292

Daniel Povey 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 Povey 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: 194 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Daniel Povey 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 Povey 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: 67 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

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

Overview

Daniel Povey is affiliated with Xiaomi (China) and works primarily in China. Their research activity spans across the field of Computer Science, with a particular emphasis on Artificial Intelligence and Signal Processing.

Their recent publications cover topics relevant to speech recognition and audio processing, including:

  • CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, arXiv (Cornell University))
  • Pruned RNN-T for fast, memory-efficient ASR training (2022, Interspeech 2022)
  • Zipformer: A faster and better encoder for automatic speech recognition (2023, arXiv (Cornell University))
  • GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio (2021, arXiv (Cornell University))
  • Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition (2023, IEEE/ACM Transactions on Audio Speech and Language Processing)

The scholar's frequent coauthors include Sanjeev Khudanpur, Zengwei Yao, Wei Kang, Liyong Guo, and Na Li, reflecting a collaborative research environment in related technical fields.

Daniel Povey has contributed notably to venues such as:

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

Book publications appear under Springer Science+Business Media, with multiple editions titled Artificial Intelligence published in 2022.

Their research addresses several topics in speech and audio technology, including:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Music and Audio Processing
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Topic Modeling
  • Advanced Data Compression Techniques

Best Publications

  • The Kaldi Speech Recognition Toolkit

    Daniel Povey;Arnab Ghoshal;Gilles Boulianne;Lukas Burget

  • Librispeech: An ASR corpus based on public domain audio books

    Vassil Panayotov;Guoguo Chen;Daniel Povey;Sanjeev Khudanpur

  • X-Vectors: Robust DNN Embeddings for Speaker Recognition

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

  • Audio augmentation for speech recognition.

    Tom Ko;Vijayaditya Peddinti;Daniel Povey;Sanjeev Khudanpur

  • A time delay neural network architecture for efficient modeling of long temporal contexts.

    Vijayaditya Peddinti;Daniel Povey;Sanjeev Khudanpur

  • MUSAN: A Music, Speech, and Noise Corpus.

    David Snyder;Guoguo Chen;Daniel Povey

  • The HTK book version 3.4

    SJ Young;G Evermann;Mjf Gales;D Kershaw

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

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

  • A study on data augmentation of reverberant speech for robust speech recognition

    Tom Ko;Vijayaditya Peddinti;Daniel Povey;Michael L. Seltzer

  • Minimum Phone Error and I-smoothing for improved discriminative training

    D. Povey;P.C. Woodland

  • Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI.

    Daniel Povey;Vijayaditya Peddinti;Daniel Galvez;Pegah Ghahremani

  • Sequence-discriminative training of deep neural networks

    Karel Veselý;Arnab Ghoshal;Lukás Burget;Daniel Povey

  • Strategies for training large scale neural network language models

    Tomas Mikolov;Anoop Deoras;Daniel Povey;Lukas Burget

  • Large scale discriminative training of hidden Markov models for speech recognition

    P.C. Woodland;D. Povey

  • Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks.

    Daniel Povey;Gaofeng Cheng;Yiming Wang;Ke Li

  • Boosted MMI for model and feature-space discriminative training

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

  • Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

    Daniel Povey;Xiaohui Zhang;Sanjeev Khudanpur

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

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

  • Improving deep neural network acoustic models using generalized maxout networks

    Xiaohui Zhang;Jan Trmal;Daniel Povey;Sanjeev Khudanpur

  • The subspace Gaussian mixture model-A structured model for speech recognition

    Daniel Povey;Lukáš Burget;Mohit Agarwal;Pinar Akyazi

  • fMPE: discriminatively trained features for speech recognition

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

Frequent Co-Authors

Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University
George Saon
George Saon IBM (United States)
Daniel Garcia-Romero
Daniel Garcia-Romero Johns Hopkins University
Brian Kingsbury
Brian Kingsbury IBM (United States)
Hagen Soltau
Hagen Soltau Google (United States)
Lukas Burget
Lukas Burget Brno University of Technology
Philip C. Woodland
Philip C. Woodland University of Cambridge
Martin Karafiat
Martin Karafiat Brno University of Technology
Thomas Hain
Thomas Hain University of Sheffield
Najim Dehak
Najim Dehak Johns Hopkins University

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