S. Umesh
- Signal Processing top 1%
- Speech and Audio Processing 78
- Music and Audio Processing 58
- Artificial Intelligence top 2%
- Speech Recognition and Synthesis 86
- Natural Language Processing Techniques 10
- Speech and dialogue systems 8
- Topic Modeling 6
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- Advanced Data Compression Techniques 7
- Image and Signal Denoising Methods 6
- Co-authors
- D.J. NelsonLeon CohenD.W. TuftsRohit SinhaK. SivakumarNitin PrasadShakti P. RathAchintya Kumar Sarkar
- Journals
- Speech Communication (5 papers)IEEE Transactions on Audio Speech and Language Processing (2 papers)IEEE/ACM Transactions on Audio Speech and Language Processing (2 papers)
- Partner nations
- IndiaUnited StatesSpain
In The Last Decade
S. Umesh
100 papers receiving 796 citations
Peers
Comparison fields: 5 of 75
- Signal Processing 595
- Artificial Intelligence 631
- Experimental and Cognitive Psychology 85
- Developmental Biology 14
- Computer Vision and Pattern Recognition 82
Countries citing papers authored by S. Umesh
This map shows the geographic impact of S. Umesh'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 S. Umesh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Umesh more than expected).
Fields of papers citing papers by S. Umesh
This network shows the impact of papers produced by S. Umesh. 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 S. Umesh. The network helps show where S. Umesh may publish in the future.
Co-authorship network
The 25 scholars most cited alongside S. Umesh, 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 | 0 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 7 | |
| 5 | 2023 | 0 | |
| 6 | 2022 | 4 | |
| 7 | 2017 | 3 | |
| 8 | 2016 | 1 | |
| 9 | 2015 | 5 | |
| 10 | 2014 | 5 | |
| 11 | 2014 | 0 | |
| 12 | Investigation of Speaker-Clustered UBMs based on Vocal Tract Lengths and MLLR matrices for Speaker Verification. | 2010 | 3 |
| 13 | 2010 | 2 | |
| 14 | 2009 | 2 | |
| 15 | 2009 | 5 | |
| 16 | Speaker-invariant features for automatic speech recognition | 2007 | 1 |
| 17 | 2007 | 21 | |
| 18 | 2002 | 4 | |
| 19 | 1999 | 43 | |
| 20 | 1992 | 4 |
About S. Umesh
S. Umesh is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 111 papers that have together received 878 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (86 papers), Speech and Audio Processing (78 papers), Music and Audio Processing (58 papers), Natural Language Processing Techniques (10 papers), Speech and dialogue systems (8 papers), Advanced Data Compression Techniques (7 papers), Image and Signal Denoising Methods (6 papers) and Topic Modeling (6 papers). The work is most often cited by research in Signal Processing (595 citations), Artificial Intelligence (631 citations) and Experimental and Cognitive Psychology (85 citations). S. Umesh has collaborated with scholars based in India, United States and Spain. Frequent co-authors include D.J. Nelson, Leon Cohen, D.W. Tufts, Rohit Sinha, K. Sivakumar, Nitin Prasad, Shakti P. Rath, Achintya Kumar Sarkar, Mark Gales and Hermann Ney. Their work appears in journals such as Speech Communication, IEEE Transactions on Audio Speech and Language Processing, IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Journal of Oceanic Engineering and IEEE Journal of Selected Topics in Signal Processing.
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.