D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 43 Citations 12,686 120 World Ranking 3986 National Ranking 2017

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Speech recognition
  • Machine learning

Heiga Zen mainly investigates Speech recognition, Speech synthesis, Hidden Markov model, Artificial intelligence and Parametric statistics. Heiga Zen has included themes like Artificial neural network, Computational linguistics and Generative model in his Speech recognition study. His biological study spans a wide range of topics, including Probabilistic logic and Discriminative model.

His studies in Speech synthesis integrate themes in fields like High fidelity and State. His work investigates the relationship between Hidden Markov model and topics such as Speaker recognition that intersect with problems in Interpolation. His Artificial intelligence research includes elements of Pattern recognition and Natural language processing.

His most cited work include:

  • WaveNet: A Generative Model for Raw Audio (2177 citations)
  • Statistical Parametric Speech Synthesis (953 citations)
  • WaveNet: A Generative Model for Raw Audio (647 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Speech recognition, Speech synthesis, Hidden Markov model, Artificial intelligence and Pattern recognition. His Speech recognition research includes themes of Artificial neural network and Generative model. His work in the fields of Speech technology overlaps with other areas such as Naturalness.

His Hidden Markov model research incorporates themes from State, Cluster analysis and Expectation–maximization algorithm. In his research on the topic of Artificial intelligence, Speech corpus is strongly related with Natural language processing. His Pattern recognition research is multidisciplinary, relying on both Estimation theory and Reduction.

He most often published in these fields:

  • Speech recognition (78.17%)
  • Speech synthesis (62.68%)
  • Hidden Markov model (61.27%)

What were the highlights of his more recent work (between 2016-2021)?

  • Speech synthesis (62.68%)
  • Speech recognition (78.17%)
  • Artificial intelligence (53.52%)

In recent papers he was focusing on the following fields of study:

Speech synthesis, Speech recognition, Artificial intelligence, Naturalness and Natural language processing are his primary areas of study. His Speech synthesis study integrates concerns from other disciplines, such as Interpretability, Prosody, Speech processing and Robustness. Heiga Zen interconnects Artificial neural network, Autoencoder, Generative model and Training set in the investigation of issues within Speech recognition.

His studies in Artificial neural network integrate themes in fields like Embedding and Benchmark. His study looks at the intersection of Generative model and topics like Text to speech synthesis with Generative grammar. His Artificial intelligence research integrates issues from Loudspeaker and Signal processing.

Between 2016 and 2021, his most popular works were:

  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis (251 citations)
  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis (148 citations)
  • LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech (116 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Speech recognition

The scientist’s investigation covers issues in Speech synthesis, Speech recognition, Artificial intelligence, Sample and Natural language processing. The concepts of his Speech synthesis study are interwoven with issues in Autoencoder, Background noise and Speech processing. His research integrates issues of Latent variable, Interpretability, Categorical variable, Mixture model and Generative model in his study of Speech recognition.

The various areas that he examines in his Artificial intelligence study include Scalability and Implementation. Natural language processing is frequently linked to Speech corpus in his study. His Deep learning study incorporates themes from Software engineering and Inference.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

WaveNet: A Generative Model for Raw Audio

Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan.
arXiv: Sound (2016)

2177 Citations

Statistical Parametric Speech Synthesis

A.W. Black;H. Zen;K. Tokuda.
international conference on acoustics, speech, and signal processing (2007)

1468 Citations

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

Heiga Zen;Takashi Nose;Junichi Yamagishi;Shinji Sako.
SSW (2007)

635 Citations

AN HMM-BASED SPEECH SYNTHESIS SYSTEM APPLIED TO ENGLISH

Keiichi Tokuda;Heiga Zen;Alan W. Black.
(2003)

488 Citations

Speech Synthesis Based on Hidden Markov Models

K. Tokuda;Y. Nankaku;T. Toda;H. Zen.
Proceedings of the IEEE (2013)

473 Citations

A Hidden Semi-Markov Model-Based Speech Synthesis System

Heiga Zen;Keiichi Tokuda;Takashi Masuko;Takao Kobayasih.
The IEICE transactions on information and systems (2007)

297 Citations

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

Heiga Zen;Hasim Sak.
international conference on acoustics, speech, and signal processing (2015)

294 Citations

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

Heiga Zen;Tomoki Toda;Masaru Nakamura;Keiichi Tokuda.
The IEICE transactions on information and systems (2007)

291 Citations

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aäron van den Oord;Yazhe Li;Igor Babuschkin;Karen Simonyan.
arXiv: Learning (2017)

275 Citations

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

J. Yamagishi;T. Nose;H. Zen;Zhen-Hua Ling.
IEEE Transactions on Audio, Speech, and Language Processing (2009)

232 Citations

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Best Scientists Citing Heiga Zen

Junichi Yamagishi

Junichi Yamagishi

National Institute of Informatics

Publications: 235

Simon King

Simon King

University of Edinburgh

Publications: 111

Tomoki Toda

Tomoki Toda

Nagoya University

Publications: 109

Zhen-Hua Ling

Zhen-Hua Ling

University of Science and Technology of China

Publications: 86

Keiichi Tokuda

Keiichi Tokuda

Nagoya Institute of Technology

Publications: 83

Haizhou Li

Haizhou Li

Chinese University of Hong Kong, Shenzhen

Publications: 67

Thierry Dutoit

Thierry Dutoit

University of Mons

Publications: 63

Satoshi Nakamura

Satoshi Nakamura

Nara Institute of Science and Technology

Publications: 47

Takao Kobayashi

Takao Kobayashi

Tokyo Institute of Technology

Publications: 44

Paavo Alku

Paavo Alku

Aalto University

Publications: 42

Tara N. Sainath

Tara N. Sainath

Google (United States)

Publications: 40

Frank K. Soong

Frank K. Soong

Microsoft (United States)

Publications: 38

Yonghui Wu

Yonghui Wu

Google (United States)

Publications: 37

Alan W. Black

Alan W. Black

Carnegie Mellon University

Publications: 36

Hiroshi Saruwatari

Hiroshi Saruwatari

University of Tokyo

Publications: 32

Mark J. F. Gales

Mark J. F. Gales

University of Cambridge

Publications: 32

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