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
Parents
434
Attention-Based Models for Speech Recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk et al. · arXiv (Cornell University) · 2015 · 1.8K citations · Full text
1–5 of 434
1.2K
22
h-index
18
h-index
14
h-index
11
h-index
10
h-index
1–5 of 1.2K
335
Google (United States)
Mountain View, United States
18 h-index
127 publications
18.3K citations

Johns Hopkins University
Baltimore, United States
16 h-index
42 publications
2.2K citations

Chinese Academy of Sciences
Beijing, China
16 h-index
61 publications
4.8K citations
University of Chinese Academy of Sciences
Beijing, China
16 h-index
38 publications
2.4K citations
Mitsubishi Electric (United States)
Cypress, United States
15 h-index
43 publications
5K citations
Microsoft (United States)
Redmond, United States
13 h-index
64 publications
3.5K citations

Northwestern Polytechnical University
Xi'an, China
13 h-index
32 publications
2.3K citations
Tencent (China)
Shenzhen, China
11 h-index
29 publications
816 citations
1–8 of 335
Repository · 50 publications · 4.1K citations
26
h-index
TASLP · 17 publications · 870 citations
14
h-index
Conference · 11 publications · 378 citations
10
h-index
9 publications · 545 citations
9
h-index
7 publications · 248 citations
7
h-index
1–5