David Suendermann‐Oeft

719 total citations
44 papers, 380 citations indexed

About

David Suendermann‐Oeft is a scholar working on Artificial Intelligence, Signal Processing and Experimental and Cognitive Psychology. According to data from OpenAlex, David Suendermann‐Oeft has authored 44 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 10 papers in Signal Processing and 7 papers in Experimental and Cognitive Psychology. Recurrent topics in David Suendermann‐Oeft's work include Speech and dialogue systems (21 papers), Speech Recognition and Synthesis (11 papers) and Natural Language Processing Techniques (11 papers). David Suendermann‐Oeft is often cited by papers focused on Speech and dialogue systems (21 papers), Speech Recognition and Synthesis (11 papers) and Natural Language Processing Techniques (11 papers). David Suendermann‐Oeft collaborates with scholars based in United States, United Kingdom and Germany. David Suendermann‐Oeft's co-authors include Vikram Ramanarayanan, Keelan Evanini, Yao Qian, Patrick Lange, Xinhao Wang, Lei Chen, Mark A. Miller, Erik Edwards, Klaus Zechner and Jidong Tao and has published in prestigious journals such as ETS Research Report Series, PubMed and National Conference on Artificial Intelligence.

In The Last Decade

David Suendermann‐Oeft

44 papers receiving 337 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Suendermann‐Oeft United States 10 275 66 55 28 28 44 380
Sophie Rosset France 15 637 2.3× 61 0.9× 65 1.2× 65 2.3× 16 0.6× 80 723
Stephen E. Levinson United States 9 136 0.5× 72 1.1× 80 1.5× 30 1.1× 15 0.5× 33 275
Fasih Haider United Kingdom 9 214 0.8× 95 1.4× 123 2.2× 39 1.4× 82 2.9× 50 477
Volker Strom United Kingdom 9 210 0.8× 111 1.7× 120 2.2× 41 1.5× 19 0.7× 22 332
Mireia Farrús Spain 14 459 1.7× 214 3.2× 150 2.7× 18 0.6× 30 1.1× 70 654
Aitzol Ezeiza Spain 8 168 0.6× 59 0.9× 48 0.9× 6 0.2× 79 2.8× 23 353
Bela Usabaev Germany 5 200 0.7× 142 2.2× 80 1.5× 13 0.5× 38 1.4× 7 329
Jangwon Kim United States 13 320 1.2× 250 3.8× 245 4.5× 25 0.9× 48 1.7× 48 591
Sylvie Ratté Canada 8 147 0.5× 34 0.5× 22 0.4× 17 0.6× 42 1.5× 34 260
György Szaszák Hungary 10 216 0.8× 92 1.4× 64 1.2× 12 0.4× 22 0.8× 38 287

Countries citing papers authored by David Suendermann‐Oeft

Since Specialization
Citations

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

Fields of papers citing papers by David Suendermann‐Oeft

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Suendermann‐Oeft. 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 David Suendermann‐Oeft. The network helps show where David Suendermann‐Oeft may publish in the future.

Co-authorship network of co-authors of David Suendermann‐Oeft

This figure shows the co-authorship network connecting the top 25 collaborators of David Suendermann‐Oeft. A scholar is included among the top collaborators of David Suendermann‐Oeft 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 David Suendermann‐Oeft. David Suendermann‐Oeft 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.
McGarry, Andrew, Jackson Liscombe, Michael Neumann, et al.. (2025). Much More Than the Malady: The Promise of a Web-Based Digital Platform Incorporating Self-Report for Research and Clinical Care in Mild Cognitive Impairment. PubMed. 3(2). 100224–100224. 1 indexed citations
2.
Kothare, Hardik, W. J. Burke, Michael Neumann, et al.. (2022). Exploring Facial Metric Normalization For Within- and Between-Subject Comparisons in a Multimodal Health Monitoring Agent. 160–165. 2 indexed citations
3.
Ramanarayanan, Vikram, Michael Neumann, Andrew Cornish, et al.. (2020). Toward Remote Patient Monitoring of Speech, Video, Cognitive and Respiratory Biomarkers Using Multimodal Dialog Technology.. Conference of the International Speech Communication Association. 492–493. 4 indexed citations
4.
Suendermann‐Oeft, David, et al.. (2019). Towards Visual Behavior Detection in Human-Machine Conversations. 36–39. 2 indexed citations
5.
Evanini, Keelan, et al.. (2018). Game-based Spoken Dialog Language Learning Applications for Young Students.. Conference of the International Speech Communication Association. 548–549. 1 indexed citations
6.
Edwards, Erik, et al.. (2018). An Automated Assistant for Medical Scribes.. Conference of the International Speech Communication Association. 3212–3213. 5 indexed citations
7.
Ramanarayanan, Vikram, et al.. (2018). Toward Scalable Dialog Technology for Conversational Language Learning: Case Study of the TOEFL® MOOC.. Conference of the International Speech Communication Association. 1960–1961. 1 indexed citations
8.
9.
Edwards, Erik, et al.. (2018). An automated medical scribe for documenting clinical encounters. 11–15. 34 indexed citations
10.
Salloum, Wael, Erik Edwards, Shabnam Ghaffarzadegan, David Suendermann‐Oeft, & Mark A. Miller. (2017). Crowdsourced Continuous Improvement of Medical Speech Recognition.. National Conference on Artificial Intelligence. 6 indexed citations
11.
Ramanarayanan, Vikram, et al.. (2017). Crowdsourcing Multimodal Dialog Interactions: Lessons Learned from the HALEF Case.. National Conference on Artificial Intelligence. 3 indexed citations
12.
Salloum, Wael, et al.. (2017). Deep Learning for Punctuation Restoration in Medical Reports. 159–164. 17 indexed citations
13.
14.
Suendermann‐Oeft, David, Vikram Ramanarayanan, Yu Zhou, et al.. (2017). A Multimodal Dialog System for Language Assessment: Current State and Future Directions. ETS Research Report Series. 2017(1). 1–7. 3 indexed citations
15.
Ramanarayanan, Vikram, et al.. (2016). Bootstrapping Development of a Cloud-Based Spoken Dialog System in the Educational Domain from Scratch Using Crowdsourced Data. Research Report. ETS RR-16-16.. ETS Research Report Series. 1 indexed citations
16.
Qian, Yao, Jidong Tao, David Suendermann‐Oeft, et al.. (2016). Noise and Metadata Sensitive Bottleneck Features for Improving Speaker Recognition with Non-Native Speech Input. 3648–3652. 7 indexed citations
17.
Zhou, Yu, Vikram Ramanarayanan, David Suendermann‐Oeft, et al.. (2015). Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech. 338–345. 64 indexed citations
18.
Suendermann‐Oeft, David, et al.. (2015). HALEF: An Open-Source Standard-Compliant Telephony-Based Modular Spoken Dialog System: A Review and An Outlook. 53–61. 9 indexed citations
19.
Ramanarayanan, Vikram, Lei Chen, Chee Wee Leong, Gang Feng, & David Suendermann‐Oeft. (2015). An analysis of time-aggregated and time-series features for scoring different aspects of multimodal presentation data. 4 indexed citations
20.
Loukina, Anastassia, et al.. (2015). Pronunciation accuracy and intelligibility of non-native speech. 1917–1921. 8 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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