U. Essen
Impact in
- Artificial Intelligence top 2%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Speech and dialogue systems
- Algorithms and Data Compression
- Bayesian Methods and Mixture Models
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
-
- Speech Recognition and Synthesis 6
- Natural Language Processing Techniques 5
- Topic Modeling 2
- Speech and dialogue systems 2
- Bayesian Modeling and Causal Inference 1
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- Speech and Audio Processing 1
- Time Series Analysis and Forecasting 1
- Co-authors
- Hermann Ney (8 shared papers)Reinhard Kneser (5 shared papers)Volker Steinbiss (5 shared papers)Reinhold Haeb‐Umbach (4 shared papers)M. Oerder (3 shared papers)Xavier Aubert (3 shared papers)
In The Last Decade
U. Essen
9 papers receiving 460 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 486
- Signal Processing 92
- Information Systems 54
- Computer Vision and Pattern Recognition 43
- Experimental and Cognitive Psychology 27
Countries citing papers authored by U. Essen
This map shows the geographic impact of U. Essen'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 U. Essen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites U. Essen more than expected).
Fields of papers citing papers by U. Essen
This network shows the impact of papers produced by U. Essen. 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 U. Essen. The network helps show where U. Essen may publish in the future.
Co-authors
The 6 scholars most cited alongside U. Essen, 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 | 1994 | 303 | |
| 2 | 1995 | 54 | |
| 3 | 1992 | 51 | |
| 4 | 1991 | 41 | |
| 5 | 1993 | 34 | |
| 6 | 1994 | 19 | |
| 7 | 1995 | 17 | |
| 8 | 1993 | 13 | |
| 9 | 1995 | 7 |
About U. Essen
U. Essen is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Infectious Diseases, having authored 9 papers that have together received 539 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (6 papers), Natural Language Processing Techniques (5 papers), Topic Modeling (2 papers), Speech and dialogue systems (2 papers), Speech and Audio Processing (1 paper), Time Series Analysis and Forecasting (1 paper), Phonetics and Phonology Research (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Artificial Intelligence (486 citations), Signal Processing (92 citations), Information Systems (54 citations), Computer Vision and Pattern Recognition (43 citations) and Experimental and Cognitive Psychology (27 citations). U. Essen has collaborated with scholars based in Germany and Finland. Frequent co-authors include Hermann Ney, Reinhard Kneser, Volker Steinbiss, Reinhold Haeb‐Umbach, M. Oerder and Xavier Aubert. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Computer Speech & Language, Speech Communication and International Journal of Pattern Recognition and Artificial Intelligence.
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.