World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
18928
World Ranking
3973
National Ranking
1891

Heiga Zen 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 Heiga Zen 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: 156 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Heiga Zen 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 Heiga Zen 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Heiga Zen is a researcher affiliated with Google in the United States, specializing in computer science with a focus on artificial intelligence and signal processing. Their scholarly work encompasses the intersection of speech and audio processing, natural language processing techniques, and related computational fields.

The scientist's main fields of study include:

  • Computer Science

Within these fields, their subfields of study are:

  • Artificial Intelligence
  • Signal Processing
  • Experimental and Cognitive Psychology
  • Computer Vision and Pattern Recognition
  • Pharmacy

The research topics addressed in their publications focus primarily on:

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

They have contributed extensively to several publication venues, with most of their work appearing in:

  • arXiv (Cornell University)
  • Interspeech 2022
  • IEEE Signal Processing Magazine
  • Information Processing & Management
  • 2022 IEEE Spoken Language Technology Workshop (SLT)

Recent notable papers authored or co-authored by Heiga Zen include:

  • "Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling," 2020, arXiv (Cornell University)
  • "MAESTRO: Matched Speech Text Representations through Modality Matching," 2022, Interspeech 2022
  • "WaveGrad: Estimating Gradients for Waveform Generation," 2020, arXiv (Cornell University)
  • "SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping," 2022, Interspeech 2022
  • "CVSS Corpus and Massively Multilingual Speech-to-Speech Translation," 2022, arXiv (Cornell University)

Heiga Zen frequently collaborates with other researchers in the field. Their most common co-authors are:

  • Yuma Koizumi
  • Bhuvana Ramabhadran
  • Yu Zhang
  • Kohei Yatabe
  • Jonathan Shen

Best Publications

  • WaveNet: A Generative Model for Raw Audio

    Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan

  • Statistical Parametric Speech Synthesis

    A.W. Black;H. Zen;K. Tokuda

  • Statistical parametric speech synthesis using deep neural networks

    Heiga Ze;Andrew Senior;Mike Schuster

  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

    Unknown

  • LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

    Heiga Zen;Viet Dang;Rob Clark;Yu Zhang

  • The HMM-based speech synthesis system (HTS) version 2.0.

    Heiga Zen;Takashi Nose;Junichi Yamagishi;Shinji Sako

  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis

    Aäron van den Oord;Yazhe Li;Igor Babuschkin;Karen Simonyan

  • Speech Synthesis Based on Hidden Markov Models

    K. Tokuda;Y. Nankaku;T. Toda;H. Zen

  • AN HMM-BASED SPEECH SYNTHESIS SYSTEM APPLIED TO ENGLISH

    Keiichi Tokuda;Heiga Zen;Alan W. Black

  • Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis

    Heiga Zen;Hasim Sak

  • A Hidden Semi-Markov Model-Based Speech Synthesis System

    Heiga Zen;Keiichi Tokuda;Takashi Masuko;Takao Kobayasih

  • Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

    Zhen-Hua Ling;Shi-Yin Kang;Heiga Zen;Andrew Senior

  • Details of the Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005

    Heiga Zen;Tomoki Toda;Masaru Nakamura;Keiichi Tokuda

  • WaveGrad: Estimating Gradients for Waveform Generation

    Nanxin Chen;Yu Zhang;Heiga Zen;Ron J Weiss

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

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

    J. Yamagishi;T. Nose;H. Zen;Zhen-Hua Ling

  • Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis

    Heiga Zen;Andrew W. Senior

  • Hidden semi-Markov model based speech synthesis.

    Heiga Zen;Keiichi Tokuda;Takashi Masuko;Takao Kobayashi

  • Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences

    Heiga Zen;Keiichi Tokuda;Tadashi Kitamura

  • Hierarchical Generative Modeling for Controllable Speech Synthesis.

    Wei-Ning Hsu;Yu Zhang;Ron J. Weiss;Heiga Zen

  • Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning

    Yu Zhang;Ron J. Weiss;Heiga Zen;Yonghui Wu

  • Deep Learning for Acoustic Modeling in Parametric Speech Generation

    Zhen-Hua Ling;Shi-yin Kang;Heiga Zen;Andrew Senior

Frequent Co-Authors

Keiichi Tokuda
Keiichi Tokuda Nagoya Institute of Technology
Tomoki Toda
Tomoki Toda Nagoya University
Yonghui Wu
Yonghui Wu Google (United States)
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics
Takashi Masuko
Takashi Masuko Preferred Networks, Inc.
Mark J. F. Gales
Mark J. F. Gales University of Cambridge
Zhifeng Chen
Zhifeng Chen Google (United States)
Yuan Cao
Yuan Cao Google (United States)
Aaron van den Oord
Aaron van den Oord Google (United States)

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Related Online Degrees & Career Pathways

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