Daan van Esch

502 total citations
12 papers, 84 citations indexed

About

Daan van Esch is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Daan van Esch has authored 12 papers receiving a total of 84 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Signal Processing. Recurrent topics in Daan van Esch's work include Natural Language Processing Techniques (9 papers), Speech Recognition and Synthesis (7 papers) and Topic Modeling (6 papers). Daan van Esch is often cited by papers focused on Natural Language Processing Techniques (9 papers), Speech Recognition and Synthesis (7 papers) and Topic Modeling (6 papers). Daan van Esch collaborates with scholars based in United States, Australia and Czechia. Daan van Esch's co-authors include Richard Sproat, M. Chua, Kanishka Rao, James R. Flynn, Pavel Golik, František Kratochvíl, T. Mark Ellison, Hywel Stoakes, Melvin Johnson and Nick Thieberger and has published in prestigious journals such as Language Resources and Evaluation, Minerva Access (University of Melbourne) and Interspeech 2022.

In The Last Decade

Daan van Esch

11 papers receiving 79 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daan van Esch United States	8	78	13	6	6	5	12	84
Simran Khanuja United States	5	97 1.2×	21 1.6×	6 1.0×	3 0.5×	2 0.4×	8	109
Aku Rouhe Finland	4	55 0.7×	17 1.3×	17 2.8×	4 0.7×	4 0.8×	14	66
Guillaume Wisniewski France	8	155 2.0×	11 0.8×	18 3.0×	2 0.3×	4 0.8×	40	162
Nathalie Camelin France	6	77 1.0×	18 1.4×	4 0.7×		3 0.6×	16	84
Sabrina J. Mielke United States	5	61 0.8×	7 0.5×	12 2.0×	3 0.5×		5	67
Yadollah Yaghoobzadeh Iran	5	59 0.8×	3 0.2×	14 2.3×	2 0.3×	9 1.8×	18	69
Isabel Papadimitriou United States	4	30 0.4×	3 0.2×	7 1.2×	2 0.3×	7 1.4×	7	37
Jungil Kong	2	40 0.5×	27 2.1×	4 0.7×		2 0.4×	2	46
Anne Vilnat France	6	95 1.2×	2 0.2×	3 0.5×	12 2.0×	5 1.0×	33	103
Nauman Dawalatabad United States	4	59 0.8×	47 3.6×	3 0.5×		5 1.0×	12	69

Countries citing papers authored by Daan van Esch

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Daan van Esch

This figure shows the co-authorship network connecting the top 25 collaborators of Daan van Esch. A scholar is included among the top collaborators of Daan van Esch 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 Daan van Esch. Daan van Esch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Conneau, Alexis, Ankur Bapna, Yu Zhang, et al.. (2022). XTREME-S: Evaluating Cross-lingual Speech Representations. Interspeech 2022. 3248–3252. 10 indexed citations

2.

Esch, Daan van, et al.. (2021). How Might We Create Better Benchmarks for Speech Recognition?. 8 indexed citations

3.

Esch, Daan van, et al.. (2020). Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages. 218–225. 1 indexed citations

4.

Esch, Daan van, et al.. (2019). Building Large-Vocabulary ASR Systems for Languages Without Any Audio Training Data. 271–275. 9 indexed citations

5.

Esch, Daan van, et al.. (2019). Future Directions in Technological Support for Language Documentation. 6 indexed citations

6.

Sproat, Richard, et al.. (2019). Unified Verbalization for Speech Recognition & Synthesis Across Languages. 3530–3534. 7 indexed citations

7.

Esch, Daan van, et al.. (2019). Developing Pronunciation Models in New Languages Faster by Exploiting Common Grapheme-to-Phoneme Correspondences Across Languages. 2100–2104. 1 indexed citations

8.

Chua, M., et al.. (2018). Text Normalization Infrastructure that Scales to Hundreds of Language Varieties. Language Resources and Evaluation. 7 indexed citations

9.

Esch, Daan van, et al.. (2018). Mining Training Data for Language Modeling Across the World's Languages. 61–65. 1 indexed citations

10.

Arnold, Josh A., Gautier Durantin, T. Mark Ellison, et al.. (2018). Building Speech Recognition Systems for Language Documentation: The CoEDL Endangered Language Pipeline and Inference System (ELPIS). Minerva Access (University of Melbourne). 205–209. 17 indexed citations

11.

Esch, Daan van & Richard Sproat. (2017). An Expanded Taxonomy of Semiotic Classes for Text Normalization. 4016–4020. 8 indexed citations

12.

Esch, Daan van, M. Chua, & Kanishka Rao. (2016). Predicting Pronunciations with Syllabification and Stress with Recurrent Neural Networks. 2841–2845. 9 indexed citations

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.

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