Matthias Sperber
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Ecology
- Signal Processing
- Co-authors
- Alex WaibelJan NiehuesGraham NeubigAlexander WaibelThanh-Le HaSatoshi NakamuraMarkus MüllerElizabeth Salesky
- Topics
- Natural Language Processing Techniques (24 papers)Speech Recognition and Synthesis (19 papers)Topic Modeling (16 papers)
- Journals
- SHILAP Revista de lepidopterologíaSpeech CommunicationLanguage Resources and Evaluation
- Partner nations
- GermanyUnited StatesJapan
In The Last Decade
Matthias Sperber
30 papers receiving 322 citations
Peers
Comparison fields: 5 of 36
- Artificial Intelligence 296
- Computer Vision and Pattern Recognition 65
- Molecular Biology 31
- Ecology 30
- Signal Processing 27
Countries citing papers authored by Matthias Sperber
This map shows the geographic impact of Matthias Sperber'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 Matthias Sperber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Sperber more than expected).
Fields of papers citing papers by Matthias Sperber
This network shows the impact of papers produced by Matthias Sperber. 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 Matthias Sperber. The network helps show where Matthias Sperber may publish in the future.
Co-authorship network of co-authors of Matthias Sperber
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Sperber. A scholar is included among the top collaborators of Matthias Sperber 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 Matthias Sperber. Matthias Sperber 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 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 42 | |
| 6 | 17 | |
| 7 | 12 | |
| 8 | XNMT: the eXtensible Neural Machine Translation toolkit | 13 |
| 9 | 6 | |
| 10 | 4 | |
| 11 | 6 | |
| 12 | 34 | |
| 13 | 4 | |
| 14 | 1 | |
| 15 | 5 | |
| 16 | 7 | |
| 17 | 1 | |
| 18 | 15 | |
| 19 | 2 | |
| 20 | 35 |
About Matthias Sperber
Matthias Sperber is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 31 papers that have together received 342 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (24 papers), Speech Recognition and Synthesis (19 papers) and Topic Modeling (16 papers). The work is most often cited by research in Artificial Intelligence (296 citations), Computer Vision and Pattern Recognition (65 citations) and Signal Processing (27 citations). Matthias Sperber has collaborated with scholars based in Germany, United States and Japan. Frequent co-authors include Alex Waibel, Jan Niehues, Graham Neubig, Alexander Waibel, Thanh-Le Ha, Satoshi Nakamura, Markus Müller, Elizabeth Salesky, Zhong Zhou and Eunah Cho. Their work appears in journals such as SHILAP Revista de lepidopterología, Speech Communication and Language Resources and Evaluation.
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.