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Tomoki Toda

Tomoki Toda

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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
59
Citations
14233
World Ranking
3416
National Ranking
30

Research.com Recognitions

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

Overview

Tomoki Toda is affiliated with Nagoya University in Japan and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and signal processing. Their research spans several subfields, including physiology, experimental and cognitive psychology, as well as computer vision and pattern recognition.

Their main areas of study are concentrated on speech recognition and synthesis, speech and audio processing, and music and audio processing. They have also conducted work related to natural language processing techniques, voice and speech disorders, topic modeling, and phonetics and phonology research.

The frequent coauthors with whom Tomoki Toda has collaborated include:

  • Wen-Chin Huang
  • Yi-Chiao Wu
  • Junichi Yamagishi
  • Tomoki Hayashi
  • Lester Phillip Violeta

Tomoki Toda's research has been published repeatedly in several venues. These include:

  • arXiv (Cornell University)
  • APSIPA Transactions on Signal and Information Processing
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • IEEE Transactions on Audio Speech and Language Processing
  • Zenodo (CERN European Organization for Nuclear Research)

Some recent publications by Tomoki Toda are as follows:

  • ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech, 2020, published in Computer Speech & Language
  • Generalization Ability of MOS Prediction Networks, 2022, presented at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Pretraining Techniques for Sequence-to-Sequence Voice Conversion, 2021, featured in IEEE/ACM Transactions on Audio Speech and Language Processing
  • LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech, 2022, presented at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Many-to-Many Voice Transformer Network, 2020, published in IEEE/ACM Transactions on Audio Speech and Language Processing

Best Publications

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

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

  • 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

  • ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

    Xin Wang;Junichi Yamagishi;Junichi Yamagishi;Massimiliano Todisco;Héctor Delgado

  • The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

    Jaime Lorenzo-Trueba;Junichi Yamagishi;Tomoki Toda;Daisuke Saito

  • Speaker-Dependent WaveNet Vocoder.

    Akira Tamamori;Tomoki Hayashi;Kazuhiro Kobayashi;Kazuya Takeda

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

    Heiga Zen;Tomoki Toda;Masaru Nakamura;Keiichi Tokuda

  • Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation (T)

    Yusuke Oda;Hiroyuki Fudaba;Graham Neubig;Hideaki Hata

  • 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

  • Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech

    Keigo Nakamura;Tomoki Toda;Hiroshi Saruwatari;Kiyohiro Shikano

  • Voice conversion algorithm based on Gaussian mixture model with dynamic frequency warping of STRAIGHT spectrum

    T. Toda;H. Saruwatari;K. Shikano

  • Statistical Voice Conversion Techniques for Body-Conducted Unvoiced Speech Enhancement

    T. Toda;M. Nakagiri;K. Shikano

  • The Voice Conversion Challenge 2016

    Tomoki Toda;Ling-Hui Chen;Daisuke Saito;Fernando Villavicencio

  • XIMERA : A new TTS from ATR based on corpus-based technologies

    Hisashi Kawai;Tomoki Toda;Jinfu Ni;Minoru Tsuzaki

  • Espnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit

    Tomoki Hayashi;Ryuichi Yamamoto;Katsuki Inoue;Takenori Yoshimura

  • Spectral conversion based on maximum likelihood estimation considering global variance of converted parameter

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

  • An Overview of Nitech HMM-based Speech Synthesis System for Blizzard Challenge 2005

    Heiga Zen;Tomoki Toda

  • Eigenvoice Conversion Based on Gaussian Mixture Model

    Tomoki Toda;Yamato Ohtani;Kiyohiro Shikano

  • Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion.

    Yi Zhao;Wen-Chin Huang;Xiaohai Tian;Junichi Yamagishi

  • Low-delay voice conversion based on maximum likelihood estimation of spectral parameter trajectory.

    Takashi Muramatsu;Yamato Ohtani;Tomoki Toda;Hiroshi Saruwatari

Frequent Co-Authors

Satoshi Nakamura
Satoshi Nakamura Nara Institute of Science and Technology
Graham Neubig
Graham Neubig Carnegie Mellon University
Sakriani Sakti
Sakriani Sakti Nara Institute of Science and Technology
Kiyohiro Shikano
Kiyohiro Shikano Nara Institute of Science and Technology
Hiroshi Saruwatari
Hiroshi Saruwatari University of Tokyo
Keiichi Tokuda
Keiichi Tokuda Nagoya Institute of Technology
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics
Kazuya Takeda
Kazuya Takeda Nagoya University
Hirokazu Kameoka
Hirokazu Kameoka NTT (Japan)
Heiga Zen
Heiga Zen Google (United States)

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