Francis Kubala
- Artificial Intelligence top 1%
- Signal Processing top 1%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Computer Networks and Communications
- Co-authors
- Richard SchwartzJohn MakhoulRalph WeischedelDaben LiuLong NguyenJ. MakhoulAmit SrivastavaToru Imai
- Topics
- Speech Recognition and Synthesis (40 papers)Music and Audio Processing (22 papers)Speech and Audio Processing (21 papers)
- Partner nations
- United StatesEgyptJapan
In The Last Decade
Francis Kubala
53 papers receiving 846 citations
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 858
- Signal Processing 496
- Computer Vision and Pattern Recognition 210
- Information Systems 114
- Computer Networks and Communications 47
Countries citing papers authored by Francis Kubala
This map shows the geographic impact of Francis Kubala'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 Francis Kubala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francis Kubala more than expected).
Fields of papers citing papers by Francis Kubala
This network shows the impact of papers produced by Francis Kubala. 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 Francis Kubala. The network helps show where Francis Kubala may publish in the future.
Co-authorship network of co-authors of Francis Kubala
This figure shows the co-authorship network connecting the top 25 collaborators of Francis Kubala. A scholar is included among the top collaborators of Francis Kubala 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 Francis Kubala. Francis Kubala is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | PERFORMANCE MEASURES FOR INFORMATION EXTRACTION | 303 |
| 3 | 4 | |
| 4 | 2 | |
| 5 | Newsroom OnTAP: real-time alerting from streaming audio | 1 |
| 6 | 1 | |
| 7 | 7 | |
| 8 | 19 | |
| 9 | 31 | |
| 10 | 1 | |
| 11 | 7 | |
| 12 | 65 | |
| 13 | 9 | |
| 14 | Named Entity Extraction From Speech | 13 |
| 15 | 40 | |
| 16 | 7 | |
| 17 | The BBN/HARC Spoken Language system | 1 |
| 18 | 8 | |
| 19 | 15 | |
| 20 | Improved speaker adaptation using text dependent spectral mappings. | 23 |
About Francis Kubala
Francis Kubala is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 57 papers that have together received 1.1k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (40 papers), Music and Audio Processing (22 papers) and Speech and Audio Processing (21 papers). The work is most often cited by research in Signal Processing (496 citations), Artificial Intelligence (858 citations) and Computer Vision and Pattern Recognition (210 citations). Francis Kubala has collaborated with scholars based in United States, Egypt and Japan. Frequent co-authors include Richard Schwartz, John Makhoul, Ralph Weischedel, Daben Liu, Long Nguyen, J. Makhoul, Amit Srivastava, Toru Imai, G. Zavaliagkos and Chris Barry. Their work appears in journals such as Proceedings of the IEEE, Communications of the ACM and ACM Computing Surveys.
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.