World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
81
Citations
26637
World Ranking
495
National Ranking
229

Computer Science

D-Index
85
Citations
29205
World Ranking
803
National Ranking
437

Chin-Hui Lee publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Chin-Hui Lee sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 504 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Chin-Hui Lee D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Chin-Hui Lee sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 81 D-Index — 93rd percentile

93% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 1997 - IEEE Fellow For contributions to automatic speech and speaker recognition.

Overview

Chin-Hui Lee is affiliated with the Georgia Institute of Technology in the United States. Their research primarily spans the fields of Computer Science, with focused work in Signal Processing, Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, and Cognitive Neuroscience.

The scientist's main topics of study include Speech and Audio Processing, Speech Recognition and Synthesis, Music and Audio Processing, Advanced Adaptive Filtering Techniques, Blind Source Separation Techniques, Hearing Loss and Rehabilitation, and Infant Health and Development.

Chin-Hui Lee has contributed to a significant number of publications, including papers in various well-known venues such as arXiv (Cornell University), IEEE/ACM Transactions on Audio Speech and Language Processing, ICASSP 2022, Interspeech 2022, and the 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP).

  • Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition, 2020, arXiv (Cornell University)
  • A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection, 2023, IEEE/ACM Transactions on Audio Speech and Language Processing
  • Information Fusion in Attention Networks Using Adaptive and Multi-Level Factorized Bilinear Pooling for Audio-Visual Emotion Recognition, 2021, IEEE/ACM Transactions on Audio Speech and Language Processing
  • A Cross-Entropy-Guided Measure (CEGM) for Assessing Speech Recognition Performance and Optimizing DNN-Based Speech Enhancement, 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression, 2020, IEEE Transactions on Signal Processing

Their research collaborations include frequent co-authorship with Jun Du, Sabato Marco Siniscalchi, Chao-Han Huck Yang, Qing Wang, and Shutong Niu.

  • Jun Du
  • Sabato Marco Siniscalchi
  • Chao-Han Huck Yang
  • Qing Wang
  • Shutong Niu

Chin-Hui Lee has published extensively in the following venues:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Interspeech 2022
  • 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP)

In 1997, Chin-Hui Lee was awarded the IEEE Fellow distinction for contributions to automatic speech and speaker recognition.

Best Publications

  • Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains

    J.-L. Gauvain;Chin-Hui Lee

  • A regression approach to speech enhancement based on deep neural networks

    Yong Xu;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • An Experimental Study on Speech Enhancement Based on Deep Neural Networks

    Yong Xu;Jun Du;Li-Rong Dai;Chin-Hui Lee

  • Minimum classification error rate methods for speech recognition

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

  • Automatic recognition of keywords in unconstrained speech using hidden Markov models

    J.G. Wilpon;L.R. Rabiner;C.-H. Lee;E.R. Goldman

  • A maximum-likelihood approach to stochastic matching for robust speech recognition

    A. Sankar;Chin-Hui Lee

  • On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

    Jun Qi;Jun Du;Sabato Marco Siniscalchi;Xiaoli Ma

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

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

  • Developments and directions in speech recognition and understanding, Part 1 [DSP Education]

    J. Baker;Li Deng;J. Glass;S. Khudanpur

  • A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films.

    Hu Chen;Hu Chen;Kailai Zhang;Peijun Lyu;Hong Li

  • A Vector Space Modeling Approach to Spoken Language Identification

    Haizhou Li;Bin Ma;Chin-Hui Lee

  • Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states

    Sadia Shakil;Chin-Hui Lee;Shella Dawn Keilholz

  • Automatic Speech and Speaker Recognition: Advanced Topics

    Chin-Hui Lee;Frank K. Soong;Kuldip K. Paliwal

  • Segmental GPD training of HMM based speech recognizer

    W. Chou;B.H. Juang;C.H. Lee

  • Acoustic modeling for large vocabulary speech recognition

    C.H. Lee;L.R. Rabiner;R. Pieraccini;J.G. Wilpon

  • Method of key-phrase detection and verification for flexible speech understanding

    Biing-Hwang Juang;Tatsuya Kawahara;Chin-Hui Lee

  • Pattern recognition using a family of design algorithms based upon the generalized probabilistic descent method

    S. Katagiri;Biing-Hwang Juang;Chin-Hui Lee

  • Vocabulary independent discriminative utterance verification for nonkeyword rejection in subword based speech recognition

    R.A. Sukkar;Chin-Hui Lee

  • The use of cohort normalized scores for speaker verification

    Unknown

  • Speaker adaptation based on MAP estimation of HMM parameters

    C.-H. Lee;J.-L. Gauvain

  • A structural Bayes approach to speaker adaptation

    K. Shinoda;C.-H. Lee

  • A frame-synchronous network search algorithm for connected word recognition

    C.-H. Lee;L.R. Rabiner

Frequent Co-Authors

Sabato Marco Siniscalchi
Sabato Marco Siniscalchi Georgia Institute of Technology
Jun Du
Jun Du University of Science and Technology of China
Biing-Hwang Juang
Biing-Hwang Juang Georgia Institute of Technology
Lawrence R. Rabiner
Lawrence R. Rabiner Rutgers, The State University of New Jersey
Jinyu Li
Jinyu Li Microsoft (United States)
Li-Rong Dai
Li-Rong Dai University of Science and Technology of China
Yu Tsao
Yu Tsao Research Center for Information Technology Innovation, Academia Sinica
Roberto Pieraccini
Roberto Pieraccini Google (United States)
Jay G. Wilpon
Jay G. Wilpon Ai Wilpon Consulting LLC
Jean-Luc Gauvain
Jean-Luc Gauvain Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

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