Daan van Esch
Impact in
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- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- Topic Modeling
- Speech and dialogue systems
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- Music and Audio Processing
- Speech and Audio Processing
Papers in
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- Natural Language Processing Techniques 9
- Speech Recognition and Synthesis 7
- Topic Modeling 6
- Speech and dialogue systems 2
- Advanced Text Analysis Techniques 1
- Authorship Attribution and Profiling 1
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- Video Analysis and Summarization 1
- Co-authors
- Richard Sproat (2 shared papers)M. Chua (2 shared papers)Kanishka Rao (1 shared paper)Pavel Golik (1 shared paper)James R. Flynn (1 shared paper)Mihir Kale (1 shared paper)Janet Wiles (1 shared paper)Yu Zhang (1 shared paper)
- Journals
- Language Resources and Evaluation (1 paper)Interspeech 2022 (1 paper)Minerva Access (University of Melbourne) (1 paper)
- Partner nations
- United StatesAustraliaCzechia
In The Last Decade
Daan van Esch
11 papers receiving 79 citations
Peers
Comparison fields: 5 of 18
- Artificial Intelligence 78
- Signal Processing 13
- Health Informatics 1
- Language and Linguistics 6
- Linguistics and Language 2
Countries citing papers authored by Daan van Esch
This map shows the geographic impact of Daan van Esch'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 Daan van Esch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daan van Esch more than expected).
Fields of papers citing papers by Daan van Esch
This network shows the impact of papers produced by Daan van Esch. 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 Daan van Esch. The network helps show where Daan van Esch may publish in the future.
Co-authors
The 25 scholars most cited alongside Daan van Esch, 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 | 2018 | 17 | |
| 2 | 2022 | 10 | |
| 3 | 2019 | 9 | |
| 4 | 2016 | 9 | |
| 5 | 2021 | 8 | |
| 6 | 2017 | 8 | |
| 7 | Text Normalization Infrastructure that Scales to Hundreds of Language Varieties | 2018 | 7 |
| 8 | 2019 | 7 | |
| 9 | 2019 | 6 | |
| 10 | Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages | 2020 | 1 |
| 11 | 2018 | 1 | |
| 12 | 2019 | 1 |
About Daan van Esch
Daan van Esch is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Experimental and Cognitive Psychology and Infectious Diseases, having authored 12 papers that have together received 84 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Speech Recognition and Synthesis (7 papers), Topic Modeling (6 papers), Speech and dialogue systems (2 papers), Advanced Text Analysis Techniques (1 paper), Authorship Attribution and Profiling (1 paper), Phonetics and Phonology Research (1 paper) and Video Analysis and Summarization (1 paper). The work is most often cited by research in Artificial Intelligence (78 citations), Signal Processing (13 citations), Health Informatics (1 citation), Language and Linguistics (6 citations) and Linguistics and Language (2 citations). Daan van Esch has collaborated with scholars based in United States, Australia and Czechia. Frequent co-authors include Richard Sproat, M. Chua, Kanishka Rao, Pavel Golik, James R. Flynn, Mihir Kale, Janet Wiles, Yu Zhang, Alexis Conneau and Simran Khanuja. Their work appears in journals such as Language Resources and Evaluation, Interspeech 2022 and Minerva Access (University of Melbourne).
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.