World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
11114
World Ranking
12374
National Ranking
5012

Overview

Chung-Cheng Chiu is a researcher affiliated with Google in the United States. Their work primarily centers on the field of Computer Science, with a significant focus on Artificial Intelligence and Signal Processing. The research contributions also extend into Computer Vision and Pattern Recognition, and to a lesser extent, Aerospace Engineering.

Their scholarly output includes numerous publications with a strong emphasis on speech and audio technologies. Main research topics involve Speech Recognition and Synthesis, Music and Audio Processing, and Speech and Audio Processing. Additional interests cover Natural Language Processing Techniques, Topic Modeling, Digital Media Forensic Detection, and Generative Adversarial Networks and Image Synthesis.

Chiu has published extensively in the following venues:

  • arXiv (Cornell University)
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
  • IEEE Journal of Selected Topics in Signal Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Applied Sciences

Some of the more recent papers authored or coauthored by Chung-Cheng Chiu include:

  • Conformer: Convolution-augmented Transformer for Speech Recognition (2020, arXiv (Cornell University))
  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU))
  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition (2020, arXiv (Cornell University))
  • BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2022, IEEE Journal of Selected Topics in Signal Processing)
  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (2023, arXiv (Cornell University))

The research collaborations include frequent coauthors such as Ruoming Pang, Yonghui Wu, Wei Han, James Qin, and Yu Zhang.

The areas of study and the research papers indicate a concentration on advancing automatic speech recognition technologies, combining methods like contrastive learning, masked language modeling, convolutional neural networks, and large-scale semi-supervised learning for enhanced audio processing systems.

Best Publications

  • SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

    Daniel S. Park;William Chan;Yu Zhang;Chung-Cheng Chiu

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

    Unknown

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • Improved Noisy Student Training for Automatic Speech Recognition

    Daniel S. Park;Yu Zhang;Ye Jia;Wei Han

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

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;James Qin;Daniel S. Park;Wei Han

  • Monotonic Chunkwise Attention

    Chung-Cheng Chiu;Colin Raffel

  • A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

  • Monotonic Infinite Lookback Attention for Simultaneous Machine Translation

    Naveen Arivazhagan;Colin Cherry;Wolfgang Macherey;Chung-Cheng Chiu

  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

    Unknown

  • Leveraging Weakly Supervised Data to Improve End-to-end Speech-to-text Translation

    Ye Jia;Melvin Johnson;Wolfgang Macherey;Ron J. Weiss

  • BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;Daniel S. Park;Wei Han;James Qin

  • Minimum Word Error Rate Training for Attention-Based Sequence-to-Sequence Models

    Rohit Prabhavalkar;Tara N. Sainath;Yonghui Wu;Patrick Nguyen

  • A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition

    Shubham Toshniwal;Anjuli Kannan;Chung-Cheng Chiu;Yonghui Wu

  • Specaugment on Large Scale Datasets

    Daniel S. Park;Yu Zhang;Chung-Cheng Chiu;Youzheng Chen

  • Two-Pass End-to-End Speech Recognition

    Sainath Tara C;Pang Ruoming;Rybach David;He Yanzhang

  • A Robust Object Segmentation System Using a Probability-Based Background Extraction Algorithm

    Unknown

  • Recognizing Long-Form Speech Using Streaming End-to-End Models

    Arun Narayanan;Rohit Prabhavalkar;Chung-Cheng Chiu;David Rybach

  • A Better and Faster end-to-end Model for Streaming ASR

    Bo Li;Anmol Gulati;Jiahui Yu;Tara N. Sainath

  • Predicting Co-verbal Gestures: A Deep and Temporal Modeling Approach

    Chung-Cheng Chiu;Louis-Philippe Morency;Stacy Marsella

  • How to train your avatar: a data driven approach to gesture generation

    Chung-Cheng Chiu;Stacy Marsella

  • State-of-the-art Speech Recognition With Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • A Comparison of End-to-End Models for Long-Form Speech Recognition

    Chung-Cheng Chiu;Anjuli Kannan;Rohit Prabhavalkar;Zhifeng Chen

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

    Tara N. Sainath;Yanzhang He;Bo Li;Arun Narayanan

Frequent Co-Authors

Yonghui Wu
Yonghui Wu Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Zhifeng Chen
Zhifeng Chen Google (United States)
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Liangliang Cao
Liangliang Cao Google (United States)
Colin Raffel
Colin Raffel University of Toronto
Stacy Marsella
Stacy Marsella Northeastern University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring related fields can open additional doors for students pursuing Computer Science in the USA. Those interested in theoretical and analytical problem-solving may want to consider an online physics bachelor's degree, which provides foundational knowledge that complements many areas in computing and engineering.

For students focused on big data and analytics, an affordable data science degree can offer specialized skills that are in high demand. Alternatively, if your interests align with hardware, systems, and device design, check out the best online electrical engineering programs USA to expand your expertise.

Besides full degrees, students may enhance their resumes and job prospects by earning easy licenses and certifications to get. These options provide quick and cost-effective pathways toward well-paying tech roles and career advancement.

Best Scientists Citing Chung-Cheng Chiu

Trending Scientists

Recently Published Articles