Hidden Markov Models

Hidden Markov Model is a stochastic process in which a sequence of observable events depends on an underlying, unobservable Markov process with a finite number of states, distinguishing it from other probabilistic models by the assumption that the system being modeled is a Markov process with hidden states and probabilistic emissions.

7.9K

Publications

521K

Citations

15.5K

Authors

3.2K

Institutions

Publications per year

2017–2026

1.3K

Authors

15.5K

  1. LD
    Li Deng

    Microsoft (United States), United States

    45 publications · 15.1K citations

    26

    h-index

  2. LR
    L. R. Rabiner

    AT&T (United States), United States

    27 publications · 32.8K citations

    25

    h-index

  3. HB
    Horst Bunke

    University of Bern, Switzerland

    32 publications · 4.4K citations

    24

    h-index

  4. CL
    Chin‐Hui Lee

    Georgia Institute of Technology, United States

    33 publications · 4.5K citations

    23

    h-index

  5. WP
    Wojciech Pieczynski

    Centre National de la Recherche Scientifique, France

    38 publications · 1.6K citations

    22

    h-index

1–5 of 15.5K

Venues

  1. IEEE Transactions on Signal Processing

    107 publications · 7.8K citations

    48

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 79 publications · 13.7K citations

    48

    h-index

  3. IEEE Transactions on Speech and Audio Processing

    113 publications · 10.5K citations

    46

    h-index

  4. 41

    h-index

  5. Pattern Recognition

    79 publications · 4.7K citations

    40

    h-index

1–5