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D-Index & Metrics

Computer Science

D-Index
56
Citations
18928
World Ranking
3973
National Ranking
1890

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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