Connectionist Temporal Classification

Connectionist Temporal Classification is a discriminative machine learning framework within the family of sequence-to-sequence models that enables end-to-end training of neural networks for temporal pattern recognition by directly optimizing the alignment between input sequences and output label sequences without requiring prior segmentation.

307

Publications

34.5K

Citations

913

Authors

239

Institutions

Publications per year

2017–2026

256

Authors

913

  1. SW
    Shinji Watanabe

    Johns Hopkins University, United States

    24 publications · 3.2K citations

    18

    h-index

  2. TH
    Takaaki Hori

    Mitsubishi Electric (United States), United States

    15 publications · 2.7K citations

    15

    h-index

  3. KR
    Kanishka Rao

    Google (United States), United States

    12 publications · 1.4K citations

    11

    h-index

  4. HS
    Haşim Sak

    Google (United States), United States

    12 publications · 1.4K citations

    11

    h-index

  5. JL
    Jinyu Li

    Microsoft (United States), United States

    10 publications · 781 citations

    10

    h-index

1–5 of 913

Venues

  1. arXiv (Cornell University)

    Repository · 41 publications · 1.9K citations

    22

    h-index

  2. IEEE/ACM Transactions on Audio Speech and Language Processing

    TASLP · 10 publications · 270 citations

    10

    h-index

  3. 8

    h-index

  4. IEEE Access

    8 publications · 310 citations

    7

    h-index

  5. 5

    h-index

1–5