Self-supervised Speech Representation Learning

Self-Supervised Speech Representation Learning is a machine learning paradigm that belongs to the broader category of speech representation learning, distinguished by its use of unlabeled speech data to pre-train models through pretext tasks that exploit intrinsic structural information within the audio signal for the purpose of deriving generalizable and transferable acoustic features.

196

Publications

15K

Citations

630

Authors

181

Institutions

Publications per year

2017–2026

194

Authors

630

  1. HL
    Hung-yi Lee

    National Taiwan University, Taiwan

    20 publications · 1.7K citations

    17

    h-index

  2. SW
    Shinji Watanabe

    Johns Hopkins University, United States

    15 publications · 1.1K citations

    15

    h-index

  3. AB
    Alexei Baevski

    Meta (Israel), Israel

    16 publications · 2.2K citations

    15

    h-index

  4. WH
    Wei-Ning Hsu

    Massachusetts Institute of Technology, United States

    12 publications · 1.2K citations

    12

    h-index

  5. MA
    Michael Auli

    Meta (Israel), Israel

    13 publications · 2K citations

    12

    h-index

1–5 of 630

Venues

  1. arXiv (Cornell University)

    Repository · 42 publications · 4.2K citations

    21

    h-index

  2. Interspeech 2022

    Conference · 20 publications · 1.2K citations

    17

    h-index

  3. 16

    h-index

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

    TASLP · 11 publications · 977 citations

    10

    h-index

  5. 2022 IEEE Spoken Language Technology Workshop (SLT)

    Conference · 8 publications · 224 citations

    8

    h-index

1–5