Zoltán Tüske
- Signal Processing top 1%
- Speech and Audio Processing 32
- Music and Audio Processing 24
- Artificial Intelligence top 2%
- Speech Recognition and Synthesis 42
- Natural Language Processing Techniques 15
- Topic Modeling 4
- Neural Networks and Applications 2
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- Advanced Data Compression Techniques 5
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- Phonetics and Phonology Research 3
- Co-authors
- Ralf SchlüterHermann NeyPavel GolikKartik AudhkhasiGeorge SaonMartin SundermeyerBrian KingsburyKazuki Irie
- Journals
- IEEE Transactions on Audio Speech and Language Processing (1 paper)Apollo (University of Cambridge) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- GermanyUnited StatesFrance
In The Last Decade
Zoltán Tüske
44 papers receiving 772 citations
Peers
Comparison fields: 5 of 69
- Signal Processing 524
- Artificial Intelligence 740
- Computer Vision and Pattern Recognition 83
- Developmental Biology 5
- Human-Computer Interaction 12
Countries citing papers authored by Zoltán Tüske
This map shows the geographic impact of Zoltán Tüske'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 Zoltán Tüske with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zoltán Tüske more than expected).
Fields of papers citing papers by Zoltán Tüske
This network shows the impact of papers produced by Zoltán Tüske. 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 Zoltán Tüske. The network helps show where Zoltán Tüske may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Zoltán Tüske, 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 | 2021 | 2 | |
| 2 | 2021 | 12 | |
| 3 | 2019 | 3 | |
| 4 | 2019 | 24 | |
| 5 | 2019 | 12 | |
| 6 | 2019 | 5 | |
| 7 | 2018 | 14 | |
| 8 | 2018 | 3 | |
| 9 | 2016 | 58 | |
| 10 | 2016 | 9 | |
| 11 | 2015 | 3 | |
| 12 | 2014 | 103 | |
| 13 | 2014 | 2 | |
| 14 | 2013 | 22 | |
| 15 | 2013 | 20 | |
| 16 | 2013 | 39 | |
| 17 | Non-stationary signal processing and its application in speech recognition. | 2012 | 3 |
| 18 | 2012 | 22 | |
| 19 | 2009 | 3 | |
| 20 | 2005 | 2 |
About Zoltán Tüske
Zoltán Tüske is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 47 papers that have together received 871 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (42 papers), Speech and Audio Processing (32 papers), Music and Audio Processing (24 papers), Natural Language Processing Techniques (15 papers), Advanced Data Compression Techniques (5 papers), Topic Modeling (4 papers), Phonetics and Phonology Research (3 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Signal Processing (524 citations), Artificial Intelligence (740 citations) and Computer Vision and Pattern Recognition (83 citations). Zoltán Tüske has collaborated with scholars based in Germany, United States and France. Frequent co-authors include Ralf Schlüter, Hermann Ney, Pavel Golik, Kartik Audhkhasi, George Saon, Martin Sundermeyer, Brian Kingsbury, Kazuki Irie, Michael Picheny and Tamer Alkhouli. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, Apollo (University of Cambridge) and arXiv (Cornell University).
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.