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
Parents
196
SUPERB: Speech Processing Universal PERformance Benchmark
Shu-Wen Yang, Po-Han Chi, Yung-Sung Chuang et al. · 2021 · 637 citations
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
Arun Babu, Changhan Wang, Andros Tjandra et al. · Interspeech 2022 · 2022 · 519 citations
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630
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181

National Taiwan University
Taipei, Taiwan
17 h-index
74 publications
10.6K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
39 publications
2.9K citations
Meta (Israel)
Tel Aviv, Israel
12 h-index
43 publications
4.1K citations

Johns Hopkins University
Baltimore, United States
9 h-index
23 publications
2.1K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
15 publications
1.1K citations
Amazon (United States)
Seattle, United States
8 h-index
11 publications
2K citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
13 publications
699 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
6 h-index
32 publications
1K citations
1–8 of 181
Repository · 42 publications · 4.2K citations
21
h-index
Conference · 20 publications · 1.2K citations
17
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Conference · 19 publications · 979 citations
16
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TASLP · 11 publications · 977 citations
10
h-index
Conference · 8 publications · 224 citations
8
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