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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
61
Citations
19955
World Ranking
3013
National Ranking
23

Keiichi Tokuda 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 Keiichi Tokuda 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: 390 publications — 86th percentile

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

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

Keiichi Tokuda 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 Keiichi Tokuda 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: 61 D-Index — 79th percentile

79% 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

  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Keiichi Tokuda is affiliated with the Nagoya Institute of Technology in Japan. The primary field of research includes computer science, with a significant focus on artificial intelligence and signal processing. Their work spans multiple subfields including plant science, mechanical engineering, and civil and structural engineering.

The scientist's research topics cover several areas related to speech and audio technologies. These topics include:

  • Speech Recognition and Synthesis
  • Speech and Audio Processing
  • Topic Modeling
  • Music and Audio Processing
  • Speech and Dialogue Systems
  • Natural Language Processing Techniques
  • Smart Agriculture and AI

Keiichi Tokuda has a publication record across multiple well-known venues. Frequent publication venues are:

  • arXiv (Cornell University)
  • IEEE Access
  • IEEE Open Journal of Signal Processing
  • Interspeech 2022
  • ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing

Recent notable papers authored or co-authored by Keiichi Tokuda include:

  • Lightweight, Multi-Speaker, Multi-Lingual Indic Text-to-Speech (2024, IEEE Open Journal of Signal Processing)
  • End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue (2022, Interspeech 2022)
  • PeriodNet: A Non-Autoregressive Raw Waveform Generative Model With a Structure Separating Periodic and Aperiodic Components (2021, IEEE Access)
  • Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanism (2021, arXiv)
  • Autoregressive Variational Autoencoder with a Hidden Semi-Markov Model-Based Structured Attention for Speech Synthesis (2022, ICASSP 2022)

Keiichi Tokuda has collaborated with several frequent co-authors, including:

  • Yoshihiko Nankaku
  • Yukiya Hono
  • Kei Hashimoto
  • Keiichiro Oura
  • Shinji Takaki

The collective body of work mainly focuses on advancing speech synthesis and recognition technologies, leveraging structured attention mechanisms, variational autoencoders, and generative models to improve raw waveform processing and multilingual text-to-speech systems.

Best Publications

  • Statistical Parametric Speech Synthesis

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

  • Speech parameter generation algorithms for HMM-based speech synthesis

    K. Tokuda;T. Yoshimura;T. Masuko;T. Kobayashi

  • Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory

    T. Toda;A.W. Black;K. Tokuda

  • Simultaneous Modeling of Spectrum, Pitch and Duration in HMM-Based Speech Synthesis

    Takayoshi Yoshimura;Keiichi Tokuda;Takashi Masuko;Takao Kobayashi

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

    Heiga Zen;Takashi Nose;Junichi Yamagishi;Shinji Sako

  • A Speech Parameter Generation Algorithm Considering Global Variance for HMM-Based Speech Synthesis

    Tomoki Toda;Keiichi Tokuda

  • Speech Synthesis Based on Hidden Markov Models

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

  • Speech parameter generation algorithm considering global variance for HMM-based speech synthesis

    Tomoki Toda;Keiichi Tokuda

  • AN HMM-BASED SPEECH SYNTHESIS SYSTEM APPLIED TO ENGLISH

    Keiichi Tokuda;Heiga Zen;Alan W. Black

  • An adaptive algorithm for mel-cepstral analysis of speech

    T. Fukada;K. Tokuda;T. Kobayashi;S. Imai

  • Hidden Markov models based on multi-space probability distribution for pitch pattern modeling

    K. Tokuda;T. Masuko;N. Miyazaki;T. Kobayashi

  • Speech parameter generation from HMM using dynamic features

    K. Tokuda;T. Kobayashi;S. Imai

  • Mel-generalized cepstral analysis - a unified approach to speech spectral estimation.

    Keiichi Tokuda;Takao Kobayashi;Takashi Masuko;Satoshi Imai

  • Multi-Space Probability Distribution HMM

    Keiichi Tokuda;Takashi Masuko;Noboru Miyazaki;Takao Kobayashi

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

    Heiga Zen;Keiichi Tokuda;Takashi Masuko;Takao Kobayasih

  • Speech synthesis using HMMs with dynamic features

    T. Masuko;K. Tokuda;T. Kobayashi;S. Imai

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

    Heiga Zen;Tomoki Toda;Masaru Nakamura;Keiichi Tokuda

  • Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model

    Tomoki Toda;Alan W. Black;Keiichi Tokuda

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

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

  • The Blizzard Challenge - 2005: Evaluating corpus-based speech synthesis on common datasets

    Alan W. Black;Keiichi Tokuda

  • Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2009

    Junichi Yamagishi;B. Usabaev;Simon King;Oliver Watts

Frequent Co-Authors

Takao Kobayashi
Takao Kobayashi Tokyo Institute of Technology
Heiga Zen
Heiga Zen Google (United States)
Takashi Masuko
Takashi Masuko Preferred Networks, Inc.
Tomoki Toda
Tomoki Toda Nagoya University
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics
Alan W. Black
Alan W. Black Carnegie Mellon University
Satoshi Nakamura
Satoshi Nakamura Nara Institute of Science and Technology
Mikko Kurimo
Mikko Kurimo Aalto University
Shigeki Sagayama
Shigeki Sagayama University of Tokyo
William Byrne
William Byrne University of Cambridge

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