Jan G. Švec

5.6k total citations · 1 hit paper
105 papers, 4.1k citations indexed

About

Jan G. Švec is a scholar working on Physiology, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Jan G. Švec has authored 105 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Physiology, 57 papers in Artificial Intelligence and 54 papers in Experimental and Cognitive Psychology. Recurrent topics in Jan G. Švec's work include Voice and Speech Disorders (83 papers), Speech Recognition and Synthesis (57 papers) and Phonetics and Phonology Research (54 papers). Jan G. Švec is often cited by papers focused on Voice and Speech Disorders (83 papers), Speech Recognition and Synthesis (57 papers) and Phonetics and Phonology Research (54 papers). Jan G. Švec collaborates with scholars based in Czechia, United States and Netherlands. Jan G. Švec's co-authors include Harm K. Schutte, Ingo R. Titze, Peter S. Popolo, Jaromı́r Horáček, Svante Granqvist, Christian T. Herbst, Anne-Maria Laukkanen, F. Šram, Donald G. Miller and Tanya L. Eadie and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Current Biology.

In The Last Decade

Jan G. Švec

98 papers receiving 4.0k citations

Hit Papers

Recommended Protocols for Instrumental Assessment of Voic... 2018 2026 2020 2023 2018 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan G. Švec Czechia 35 3.3k 1.9k 1.6k 1.4k 760 105 4.1k
Eric J. Hunter United States 28 2.5k 0.8× 1.2k 0.6× 1.0k 0.6× 1.1k 0.8× 693 0.9× 163 3.4k
Brad H. Story United States 32 2.4k 0.7× 2.1k 1.1× 2.0k 1.3× 631 0.5× 1.3k 1.7× 126 3.9k
Ronald C. Scherer United States 29 2.3k 0.7× 1.4k 0.7× 1.5k 0.9× 687 0.5× 449 0.6× 140 2.9k
Ulrich Eysholdt Germany 34 2.7k 0.8× 1.3k 0.7× 1.4k 0.9× 1.1k 0.8× 581 0.8× 155 3.6k
Bruce R. Gerratt United States 37 4.6k 1.4× 3.1k 1.6× 2.0k 1.3× 1.9k 1.4× 836 1.1× 122 5.4k
Anne-Maria Laukkanen Finland 34 2.3k 0.7× 1.6k 0.8× 956 0.6× 1.1k 0.8× 401 0.5× 127 3.0k
Elmar Nöth Germany 37 1.8k 0.6× 1.5k 0.8× 2.7k 1.8× 348 0.3× 1.5k 1.9× 295 4.9k
James Hillenbrand United States 26 1.8k 0.5× 3.6k 1.8× 2.0k 1.3× 746 0.5× 1.3k 1.8× 63 4.9k
Harry Hollien United States 36 1.8k 0.5× 1.9k 1.0× 1.4k 0.9× 432 0.3× 656 0.9× 146 3.6k
R.J. Baken United States 17 1.6k 0.5× 1.2k 0.6× 719 0.5× 640 0.5× 415 0.5× 42 2.3k

Countries citing papers authored by Jan G. Švec

Since Specialization
Citations

This map shows the geographic impact of Jan G. Švec'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 Jan G. Švec with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan G. Švec more than expected).

Fields of papers citing papers by Jan G. Švec

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jan G. Švec. 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 Jan G. Švec. The network helps show where Jan G. Švec may publish in the future.

Co-authorship network of co-authors of Jan G. Švec

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

All Works

20 of 20 papers shown
1.
Švec, Jan G. & Zhaoyan Zhang. (2025). Application of nonlinear dynamics theory to understanding normal and pathologic voices in humans. Philosophical Transactions of the Royal Society B Biological Sciences. 380(1923). 20240018–20240018. 10 indexed citations
2.
Šorel, Michal, et al.. (2023). Automatic estimation of mucosal waves lateral peak sharpness – Modern approach. Electronic Imaging. 35(9). 300–1. 1 indexed citations
3.
Švec, Jan G., et al.. (2023). Validation of the Czech Version of the Voice Handicap Index. Journal of Voice. 39(5). 1415.e7–1415.e14. 3 indexed citations
4.
Herbst, Christian T., et al.. (2023). Domestic cat larynges can produce purring frequencies without neural input. Current Biology. 33(21). 4727–4732.e4. 9 indexed citations
5.
Švec, Jan G., et al.. (2021). Subglottal pressure oscillations in anechoic and resonant conditions and their influence on excised larynx phonations. Scientific Reports. 11(1). 28–28. 7 indexed citations
6.
Simberg, Susanna, et al.. (2017). Resonance Tube Phonation in Water—the Effect of Tube Diameter and Water Depth on Back Pressure and Bubble Characteristics at Different Airflows. Journal of Voice. 32(1). 126.e11–126.e22. 20 indexed citations
7.
Herbst, Christian T. & Jan G. Švec. (2014). Adjustment of Glottal Configurations in Singing. Journal of Singing. 70(3). 301. 8 indexed citations
8.
Švec, Jan G. & Harm K. Schutte. (2012). Kymographic imaging of laryngeal vibrations. Current Opinion in Otolaryngology & Head & Neck Surgery. 20(6). 458–465. 43 indexed citations
9.
Kunduk, Melda, et al.. (2012). Vocal Fold Vibratory Behavior Changes following Surgical Treatment of Polyps Investigated with High-Speed Videoendoscopy and Phonovibrography. Annals of Otology Rhinology & Laryngology. 121(6). 355–363. 14 indexed citations
10.
Vampola, Tomáš, et al.. (2011). Finite element modelling of vocal tract changes after voice therapy. Applied and Computational Mechanics. 5(1). 6 indexed citations
11.
Švec, Jan G., et al.. (2009). Basic requirements on microphones for voice recordings.. 157–160. 1 indexed citations
12.
Šidlof, Petr, et al.. (2008). Geometry of human vocal folds and glottal channel for mathematical and biomechanical modeling of voice production. Journal of Biomechanics. 41(5). 985–995. 31 indexed citations
13.
Švec, Jan G., et al.. (2007). Mucosal waves on the vocal folds: conceptualization based on videokymography.. 171–172. 1 indexed citations
14.
Švec, Jan G., et al.. (2006). Protocol Challenges for On-the-Job Voice Dosimetry of Teachers in the United States and Finland. Journal of Voice. 21(4). 385–396. 20 indexed citations
15.
Hunter, Eric J., Jan G. Švec, & Ingo R. Titze. (2005). Comparison of the Produced and Perceived Voice Range Profiles in Untrained and Trained Classical Singers. Journal of Voice. 20(4). 513–526. 25 indexed citations
16.
Švec, Jan G., Peter S. Popolo, & Ingo R. Titze. (2003). Measurement of vocal doses in speech: experimental procedure and signal processing. Logopedics Phoniatrics Vocology. 28(4). 181–192. 95 indexed citations
17.
Miller, Donald G., Jan G. Švec, & H.K. Schutte. (2002). Measurement of Characteristic Leap Interval Between Chest and Falsetto Registers. Journal of Voice. 16(1). 8–19. 32 indexed citations
18.
Horáček, Jaromı́r, et al.. (2001). Measurement of the vocal-fold vibration behaviour in excised human larynges.. 2–7. 5 indexed citations
19.
Horáček, Jaromı́r, et al.. (2001). Mathematical modelling of male vocal tract.. 69–74.
20.
Schutte, Harm K., Jan G. Švec, & F. Šram. (1998). First results of clinical application of videokymography. The Laryngoscope. 108(8). 1206–1210. 94 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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