Tom Bagby
Impact in
- Signal Processing top 5%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 5%
- Speech Recognition and Synthesis
- Natural Language Processing Techniques
- Topic Modeling
- Speech and dialogue systems
Papers in
-
- Speech Recognition and Synthesis 6
- Topic Modeling 3
- Natural Language Processing Techniques 1
- Speech and dialogue systems 1
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- Music and Audio Processing 5
- Speech and Audio Processing 4
- Co-authors
- Khe Chai Sim (3 shared papers)Kanishka Rao (2 shared papers)Tara N. Sainath (2 shared papers)Bo Li (1 shared paper)Ruoming Pang (1 shared paper)Qiao Liang (1 shared paper)Ding Zhao (1 shared paper)Raziel Álvarez (1 shared paper)
- Journals
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (1 paper)
- Partner nations
- United States
In The Last Decade
Tom Bagby
7 papers receiving 373 citations
Tom Bagby's Hit Papers
Peers
Comparison fields: 5 of 44
- Signal Processing 252
- Artificial Intelligence 376
- Hardware and Architecture 14
- Computer Vision and Pattern Recognition 36
- Human-Computer Interaction 6
Countries citing papers authored by Tom Bagby
This map shows the geographic impact of Tom Bagby's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tom Bagby with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Bagby more than expected).
Fields of papers citing papers by Tom Bagby
This network shows the impact of papers produced by Tom Bagby. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tom Bagby. The network helps show where Tom Bagby may publish in the future.
Co-authors
The 25 scholars most cited alongside Tom Bagby, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Streaming End-to-end Speech Recognition for Mobile Devices Hit paper breakdown → | 2019 | 363 |
| 2 | 2018 | 16 | |
| 3 | 2017 | 14 | |
| 4 | 2022 | 12 | |
| 5 | 2018 | 7 | |
| 6 | 2018 | 6 | |
| 7 | 2017 | 6 |
About Tom Bagby
Tom Bagby is a scholar working on Artificial Intelligence, Signal Processing, Cancer Research, Infectious Diseases and Organic Chemistry, having authored 7 papers that have together received 424 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (6 papers), Music and Audio Processing (5 papers), Speech and Audio Processing (4 papers), Topic Modeling (3 papers), Cancer-related molecular mechanisms research (1 paper), Natural Language Processing Techniques (1 paper) and Speech and dialogue systems (1 paper). The work is most often cited by research in Signal Processing (252 citations), Artificial Intelligence (376 citations), Hardware and Architecture (14 citations), Computer Vision and Pattern Recognition (36 citations) and Human-Computer Interaction (6 citations). Tom Bagby has collaborated with scholars based in United States. Frequent co-authors include Khe Chai Sim, Kanishka Rao, Tara N. Sainath, Bo Li, Ruoming Pang, Qiao Liang, Ding Zhao, Raziel Álvarez, Deepti Bhatia and Yanzhang He. Their work appears in journals such as ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.