World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Germany
2023

D-Index & Metrics

Computer Science

D-Index
70
Citations
31968
World Ranking
1826
National Ranking
929

Research.com Recognitions

  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2013 - Fellow, National Academy of Inventors
  • 2004 - Member of the National Academy of Engineering For contributions to speech coding and speech recognition.
  • 1992 - IEEE Fellow For contributions to the theory of vector quantization and its application to coding and automatic recognition of speech.

Overview

Biing-Hwang Juang is affiliated with the Georgia Institute of Technology in the United States. Their primary field of study is Computer Science, with contributions that encompass several subfields including Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Human-Computer Interaction, and Control and Systems Engineering.

The research topics they have worked on include:

  • Speech and dialogue systems
  • Wireless Signal Modulation Classification
  • Speech Recognition and Synthesis
  • Topic Modeling
  • Hand Gesture Recognition Systems
  • Human Pose and Action Recognition
  • Human Motion and Animation

Recent papers authored or co-authored by Biing-Hwang Juang cover developments in deep learning and wireless communication technologies as well as voice authentication methods. Notable publications include:

  • "Deep Learning Enabled Semantic Communication Systems," 2021, IEEE Transactions on Signal Processing
  • "Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels," 2020, IEEE Transactions on Wireless Communications
  • "Deep Learning Based End-to-End Wireless Communication Systems Without Pilots," 2021, IEEE Transactions on Cognitive Communications and Networking
  • "Active voice authentication," 2020, Digital Signal Processing
  • "Accretionary Learning With Deep Neural Networks With Applications," 2023, IEEE Transactions on Cognitive Communications and Networking

Frequent co-authors collaborating with Biing-Hwang Juang include Geoffrey Ye Li, Ruolin Su, Mingyu Chen, Ghassan AlRegib, and Hao Ye.

The scientist's work has been published most often in venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Cognitive Communications and Networking
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Transactions on Signal Processing
  • IEEE Transactions on Wireless Communications

Awards and recognitions received by Biing-Hwang Juang include:

  • Fellow, National Academy of Inventors (2013)
  • Member of the National Academy of Engineering (2004) for contributions to speech coding and speech recognition
  • IEEE Fellow (1992) for contributions to the theory of vector quantization and its application to coding and automatic recognition of speech

Best Publications

  • An introduction to hidden Markov models

    L. Rabiner;B. Juang

  • Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

    Hao Ye;Geoffrey Ye Li;Biing-Hwang Juang

  • Deep Learning Enabled Semantic Communication Systems

    Huiqiang Xie;Zhijin Qin;Geoffrey Ye Li;Biing-Hwang Juang

  • Discriminative learning for minimum error classification (pattern recognition)

    B.-H. Juang;S. Katagiri

  • Minimum classification error rate methods for speech recognition

    Biing-Hwang Juang;Wu Hou;Chin-Hui Lee

  • Hidden Markov models for speech recognition

    B. H. Juang;L. R. Rabiner

  • Deep Reinforcement Learning Based Resource Allocation for V2V Communications

    Hao Ye;Geoffrey Ye Li;Biing-Hwang Fred Juang

  • Signal Processing in Cognitive Radio

    Jun Ma;G.Y. Li;Biing Hwang Juang

  • A vector quantization approach to speaker recognition

    F. Soong;A. Rosenberg;L. Rabiner;B. Juang

  • Line spectrum pair (LSP) and speech data compression

    F. Soong;B. Juang

  • The segmental K-means algorithm for estimating parameters of hidden Markov models

    B.-H. Juang;L.R. Rabiner

  • A probabilistic distance measure for hidden Markov models

    B.-H. Juang;L. R. Rabiner

  • Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction

    Tomohiro Nakatani;Takuya Yoshioka;Keisuke Kinoshita;Masato Miyoshi

  • On the use of bandpass liftering in speech recognition

    Biing-Hwang Juang;L. Rabiner;J. Wilpon

  • A study on speaker adaptation of the parameters of continuous density hidden Markov models

    C.-H. Lee;C.-H. Lin;B.-H. Juang

  • Mixture autoregressive hidden Markov models for speech signals

    Biing-Hwang Juang;L. Rabiner

  • Multiple stage vector quantization for speech coding

    Biing-Hwang Juang;A. Gray

  • Recognition of isolated digits using hidden Markov models with continuous mixture densities

    L. R. Rabiner;B.-H. Juang;S. E. Levinson;M. M. Sondhi

  • Recognition unit model training based on competing word and word string models

    Wu Chou;Biing-Hwang Juang

  • Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains

    B.-H. Juang

  • Discriminative utterance verification for connected digits recognition

    M.G. Rahim;Chin-Hui Lee;Biing-Hwang Juang

Frequent Co-Authors

Chin-Hui Lee
Chin-Hui Lee Georgia Institute of Technology
Lawrence R. Rabiner
Lawrence R. Rabiner Rutgers, The State University of New Jersey
Jay G. Wilpon
Jay G. Wilpon Ai Wilpon Consulting LLC
Stephen E. Levinson
Stephen E. Levinson University of Illinois at Urbana-Champaign
Tatsuya Kawahara
Tatsuya Kawahara Kyoto University
Frank K. Soong
Frank K. Soong Microsoft Research Asia (China)
Aaron E. Rosenberg
Aaron E. Rosenberg AT&T (United States)
Kuansan Wang
Kuansan Wang Microsoft (United States)
Kuldip K. Paliwal
Kuldip K. Paliwal Griffith University
Richard Rose
Richard Rose Google (United States)

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 Computer Science in the USA often opens doors to diverse online education and career opportunities in related fields. Many students choose to enhance their skills or shift focus through flexible programs that align with high-growth industries.

For those interested in the field of justice, a cheap criminal justice degree offers an affordable pathway into law enforcement, cybersecurity, and public policy. If you’re business-minded, consider a cheapest accredited online accounting degree to build expertise in financial systems, which can complement computer science skills in fintech or analytics.

Tech professionals looking to advance their careers might pursue an online master data science, ideal for those aiming to specialize in artificial intelligence, big data, or analytics. Additionally, an online degree for construction management can lead to careers managing technology-driven construction projects and smart cities.

These related programs support a variety of career pathways, letting students combine interests and respond to evolving job market needs.

Best Scientists Citing Biing-Hwang Juang

Trending Scientists