Attention-based Speech Recognition

Attention-Based Speech Recognition is a speech recognition framework that employs attention mechanisms to dynamically align and weight acoustic features over time, enabling more accurate and context-sensitive modeling of variable-length input sequences.

434

Publications

29.4K

Citations

1.2K

Authors

335

Institutions

Publications per year

2017–2026

404

Authors

1.2K

  1. SW
    Shinji Watanabe

    Johns Hopkins University, United States

    31 publications · 3.6K citations

    22

    h-index

  2. TH
    Takaaki Hori

    Mitsubishi Electric (United States), United States

    22 publications · 2.9K citations

    18

    h-index

  3. JL
    Jinyu Li

    Microsoft (United States), United States

    15 publications · 914 citations

    14

    h-index

  4. YG
    Yashesh Gaur

    Microsoft (United States), United States

    11 publications · 501 citations

    11

    h-index

  5. JC
    Jan Chorowski

    University of Wrocław, Poland

    10 publications · 3.4K citations

    10

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 50 publications · 4.1K citations

    26

    h-index

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

    TASLP · 17 publications · 870 citations

    14

    h-index

  3. 10

    h-index

  4. 9

    h-index

  5. Sensors

    7 publications · 248 citations

    7

    h-index

1–5