Jonathan Raiman
Impact in
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 5%
- Topic Modeling
- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- Advanced Graph Neural Networks
- Speech and dialogue systems
Papers in
-
- Topic Modeling 4
- Natural Language Processing Techniques 3
- Speech Recognition and Synthesis 2
- Sentiment Analysis and Opinion Mining 1
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- Speech and Audio Processing 2
- Co-authors
- Olivier Raiman (2 shared papers)J. J. Miller (2 shared papers)Andrew Gibiansky (2 shared papers)Sercan Ö. Arık (2 shared papers)Gregory Diamos (2 shared papers)Yanqi Zhou (1 shared paper)Wei Ping (1 shared paper)Kainan Peng (1 shared paper)
- Journals
- International Conference on Machine Learning (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Jonathan Raiman
6 papers receiving 243 citations
Peers
Comparison fields: 5 of 33
- Signal Processing 80
- Artificial Intelligence 239
- Management Science and Operations Research 43
- Computer Vision and Pattern Recognition 24
- Hardware and Architecture 5
Countries citing papers authored by Jonathan Raiman
This map shows the geographic impact of Jonathan Raiman'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 Jonathan Raiman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Raiman more than expected).
Fields of papers citing papers by Jonathan Raiman
This network shows the impact of papers produced by Jonathan Raiman. 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 Jonathan Raiman. The network helps show where Jonathan Raiman may publish in the future.
Co-authors
The 17 scholars most cited alongside Jonathan Raiman, 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 | 2018 | 100 | |
| 2 | Deep Voice 2: Multi-Speaker Neural Text-to-Speech. | 2017 | 88 |
| 3 | Deep Voice: Real-time Neural Text-to-Speech | 2017 | 40 |
| 4 | 2018 | 29 | |
| 5 | 2021 | 8 | |
| 6 | 2022 | 4 |
About Jonathan Raiman
Jonathan Raiman is a scholar working on Artificial Intelligence, Signal Processing, Hardware and Architecture, Management Science and Operations Research and Electrical and Electronic Engineering, having authored 6 papers that have together received 269 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Speech and Audio Processing (2 papers), Speech Recognition and Synthesis (2 papers), Data Quality and Management (1 paper), Sentiment Analysis and Opinion Mining (1 paper), VLSI and FPGA Design Techniques (1 paper) and VLSI and Analog Circuit Testing (1 paper). The work is most often cited by research in Signal Processing (80 citations), Artificial Intelligence (239 citations), Management Science and Operations Research (43 citations), Computer Vision and Pattern Recognition (24 citations) and Hardware and Architecture (5 citations). Jonathan Raiman has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Olivier Raiman, J. J. Miller, Andrew Gibiansky, Sercan Ö. Arık, Gregory Diamos, Yanqi Zhou, Wei Ping, Kainan Peng, Yongguo Kang and Andrew Y. Ng. Their work appears in journals such as International Conference on Machine Learning, Proceedings of the AAAI Conference on Artificial Intelligence, arXiv (Cornell University) and Neural Information Processing Systems.
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.