Karthik Visweswariah

1.4k total citations
70 papers, 938 citations indexed

About

Karthik Visweswariah is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Karthik Visweswariah has authored 70 papers receiving a total of 938 indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Artificial Intelligence, 28 papers in Signal Processing and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Karthik Visweswariah's work include Speech Recognition and Synthesis (29 papers), Speech and Audio Processing (26 papers) and Topic Modeling (18 papers). Karthik Visweswariah is often cited by papers focused on Speech Recognition and Synthesis (29 papers), Speech and Audio Processing (26 papers) and Topic Modeling (18 papers). Karthik Visweswariah collaborates with scholars based in United States, India and Australia. Karthik Visweswariah's co-authors include Brian Kingsbury, George Saon, Dimitri Kanevsky, Bhuvana Ramabhadran, Daniel Povey, Sanjeev R. Kulkarni, Sergio Verdú, Peder A. Olsen, Vaibhava Goel and Scott Axelrod and has published in prestigious journals such as IEEE Transactions on Information Theory, IBM Journal of Research and Development and IEEE Transactions on Audio Speech and Language Processing.

In The Last Decade

Karthik Visweswariah

67 papers receiving 832 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Karthik Visweswariah United States 14 769 392 132 115 68 70 938
Zengfeng Huang China 12 487 0.6× 153 0.4× 158 1.2× 128 1.1× 246 3.6× 44 726
Kristie Seymore United States 10 960 1.2× 94 0.2× 167 1.3× 321 2.8× 119 1.8× 12 1.2k
Kuiyu Chang Singapore 19 795 1.0× 117 0.3× 195 1.5× 451 3.9× 133 2.0× 45 1.1k
Rafael C. Carrasco Spain 15 467 0.6× 98 0.3× 147 1.1× 89 0.8× 50 0.7× 36 683
Qipeng Guo China 11 907 1.2× 102 0.3× 219 1.7× 118 1.0× 58 0.9× 22 1.1k
Francis Kubala United States 16 858 1.1× 496 1.3× 210 1.6× 114 1.0× 47 0.7× 57 1.1k
M. D. McIlroy United States 13 376 0.5× 104 0.3× 125 0.9× 166 1.4× 179 2.6× 22 706
Takanori Maehara Japan 15 298 0.4× 68 0.2× 104 0.8× 71 0.6× 92 1.4× 49 535
Colin de la Higuera France 11 571 0.7× 92 0.2× 124 0.9× 113 1.0× 93 1.4× 49 828

Countries citing papers authored by Karthik Visweswariah

Since Specialization
Citations

This map shows the geographic impact of Karthik Visweswariah'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 Karthik Visweswariah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karthik Visweswariah more than expected).

Fields of papers citing papers by Karthik Visweswariah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Karthik Visweswariah. 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 Karthik Visweswariah. The network helps show where Karthik Visweswariah may publish in the future.

Co-authorship network of co-authors of Karthik Visweswariah

This figure shows the co-authorship network connecting the top 25 collaborators of Karthik Visweswariah. A scholar is included among the top collaborators of Karthik Visweswariah based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Karthik Visweswariah. Karthik Visweswariah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Khapra, Mitesh M., et al.. (2014). When Transliteration Met Crowdsourcing : An Empirical Study of Transliteration via Crowdsourcing using Efficient, Non-redundant and Fair Quality Control. Language Resources and Evaluation. 196–202. 12 indexed citations
2.
Visweswariah, Karthik, et al.. (2013). Semi-Supervised Answer Extraction from Discussion Forums. International Joint Conference on Natural Language Processing. 1–9. 7 indexed citations
3.
Visweswariah, Karthik, et al.. (2013). Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation. Meeting of the Association for Computational Linguistics. 1275–1284. 2 indexed citations
4.
Khapra, Mitesh M., et al.. (2013). Improving reordering performance using higher order and structural features. North American Chapter of the Association for Computational Linguistics. 315–324. 5 indexed citations
5.
Visweswariah, Karthik, et al.. (2012). A Study of Word-Classing for MT Reordering. Language Resources and Evaluation. 3971–3976.
6.
Singh, Amit, et al.. (2012). Does Similarity Matter? The Case of Answer Extraction from Technical Discussion Forums. International Conference on Computational Linguistics. 175–184. 7 indexed citations
7.
Khapra, Mitesh M., et al.. (2012). Report of the Shared Task on Learning Reordering from Word Alignments at RSMT 2012. International Conference on Computational Linguistics. 9–16. 1 indexed citations
8.
Navrátil, Jiří, et al.. (2012). A Comparison of Syntactic Reordering Methods for English-German Machine Translation. International Conference on Computational Linguistics. 2043–2058. 8 indexed citations
9.
Bhattacharyya, Pushpak, et al.. (2011). Clause-Based Reordering Constraints to Improve Statistical Machine Translation. International Joint Conference on Natural Language Processing. 1351–1355. 4 indexed citations
10.
Visweswariah, Karthik, et al.. (2011). A Word Reordering Model for Improved Machine Translation. Empirical Methods in Natural Language Processing. 486–496. 34 indexed citations
11.
Gandhe, Ankur, et al.. (2011). Handling verb phrase morphology in highly inflected Indian languages for Machine Translation. International Joint Conference on Natural Language Processing. 111–119. 5 indexed citations
12.
Singh, Amit & Karthik Visweswariah. (2011). CQC. 2033–2036. 5 indexed citations
13.
Visweswariah, Karthik, Jiří Navrátil, Jeffrey Sorensen, Vijil Chenthamarakshan, & Nandakishore Kambhatla. (2010). Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation. International Conference on Computational Linguistics. 1119–1127. 28 indexed citations
14.
Visweswariah, Karthik, Vijil Chenthamarakshan, & Nandakishore Kambhatla. (2010). Urdu and Hindi: Translation and sharing of linguistic resources. International Conference on Computational Linguistics. 1283–1291. 7 indexed citations
15.
Deshmukh, Om, et al.. (2010). Role of language models in spoken fluency evaluation. 2866–2869. 1 indexed citations
16.
Park, Youngja, Siddharth Patwardhan, Karthik Visweswariah, & Stephen C. Gates. (2008). An empirical analysis of word error rate and keyword error rate. 2070–2073. 45 indexed citations
17.
Povey, Daniel, Dimitri Kanevsky, Brian Kingsbury, et al.. (2008). Boosted MMI for model and feature-space discriminative training. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 4057–4060. 250 indexed citations
18.
Huang, Jing, Etienne Marcheret, Karthik Visweswariah, & Gerasimos Potamianos. (2007). The IBM RT07 Evaluation Systems for Speaker Diarization on Lecture Meetings.. 497–508. 3 indexed citations
19.
Huang, Jing, Etienne Marcheret, & Karthik Visweswariah. (2005). Rapid Feature Space Speaker Adaptation for Multi-Stream HMM-Based Audio-Visual Speech Recognition. 338–341. 9 indexed citations
20.
Visweswariah, Karthik & Ramesh A. Gopinath. (2004). Adaptation of front end parameters in a speech recognizer. 21–24. 9 indexed citations

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.

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