Shankar Kumar
- Artificial Intelligence top 0.5%
- Topic Modeling 23
- Natural Language Processing Techniques 22
- Speech Recognition and Synthesis 9
- Text Readability and Simplification 7
- Algorithms and Data Compression 6
- Speech and dialogue systems 3
- Signal Processing top 5%
- Speech and Audio Processing 2
- Music and Audio Processing 2
- Information Systems top 2%
Shankar Kumar
29 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 1.6k
- Signal Processing 252
- Information Systems 268
- Computer Vision and Pattern Recognition 218
- Statistical and Nonlinear Physics 55
Countries citing papers authored by Shankar Kumar
This map shows the geographic impact of Shankar Kumar'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 Shankar Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shankar Kumar more than expected).
Fields of papers citing papers by Shankar Kumar
This network shows the impact of papers produced by Shankar Kumar. 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 Shankar Kumar. The network helps show where Shankar Kumar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shankar Kumar, 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 | 2024 | 0 | |
| 2 | 2023 | 1 | |
| 3 | Data Strategies for Low-Resource Grammatical Error Correction | 2021 | 9 |
| 4 | 2021 | 5 | |
| 5 | Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Lossbreakdown → | 2020 | 277 |
| 6 | 2018 | 2 | |
| 7 | 2018 | 3 | |
| 8 | 2015 | 47 | |
| 9 | Expected Sequence Similarity Maximization | 2010 | 3 |
| 10 | Model Combination for Machine Translation | 2010 | 21 |
| 11 | 2008 | 296 | |
| 12 | 2008 | 88 | |
| 13 | Improving Word Alignment with Bridge Languages | 2007 | 36 |
| 14 | 2006 | 25 | |
| 15 | Minimum bayes-risk techniques in automatic speech recognition and statistical machine translation | 2005 | 3 |
| 16 | 2005 | 47 | |
| 17 | 2004 | 191 | |
| 18 | 2003 | 46 | |
| 19 | 2002 | 13 | |
| 20 | 2000 | 20 |
About Shankar Kumar
Shankar Kumar is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Information Systems, having authored 30 papers that have together received 1.8k indexed citations. Recurring topics across this work include Topic Modeling (23 papers), Natural Language Processing Techniques (22 papers), Speech Recognition and Synthesis (9 papers), Text Readability and Simplification (7 papers), Algorithms and Data Compression (6 papers), Speech and dialogue systems (3 papers), Speech and Audio Processing (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Artificial Intelligence (1.6k citations), Signal Processing (252 citations), Information Systems (268 citations), Computer Vision and Pattern Recognition (218 citations) and Statistical and Nonlinear Physics (55 citations). Shankar Kumar has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Bill Byrne, Franz Josef Och, Wolfgang Macherey, Qian Zhang, Erik McDermott, Haşim Sak, Anshuman Tripathi, Lu Han, Mohamed Aly and Richard Sproat. Their work appears in journals such as Natural Language Engineering, Tetrahedron Letters, Computer Speech & Language, Empirical Methods in Natural Language Processing and North American Chapter of the Association for Computational Linguistics.
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.